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AI Post Transformers
By mcgrofTechnology
AI Post Transformers episodes
- AI+HW 2035: Co-Designing Efficient AI SystemsThis episode explores the AI+HW 2035 roadmap, arguing that the next decade of AI progress will depend less on raw compute growth and more on coordinated design across models, compilers, runtimes, memory systems, and chips. It breaks down the memory wall in concrete terms, showing how moving weights, activations, and KV caches can cost more time and energy than the math itself, especially for inference, autoregressive serving, and state-heavy workloads like video world models. The discussion examines quantization, mixed precision, sparsity, pruning, distillation, tiered memory, IO-aware attention, and hardware-aware scheduling, with the key claim that these methods only matter when the full stack preserves locality and avoids wasteful data movement. Listeners would find it interesting because it treats AI efficiency as a practical systems problem and policy agenda, not just a matter of inventing better model architectures.Sources:1. AI+HW 2035: Shaping the Next Decade — Deming Chen, Jason Cong, Azalia Mirhoseini, Christos Kozyrakis, Subhasish Mitra, Jinjun Xiong, Cliff Young, Anima Anandkumar, Michael Littman, Aron Kirschen, Sophia Shao, Serge Leef, Naresh Shanbhag, Dejan Milojicic, Michael Schulte, Gert Cauwenberghs, Jerry M. Chow, Tri Dao, Kailash Gopalakrishnan, Richard Ho, Hoshik Kim, Kunle Olukotun, David Z. Pan, Mark Ren, Dan Roth, Aarti Singh, Yizhou Sun, Yusu Wang, Yann LeCun, Ruchir Puri, 2026http://arxiv.org/abs/2603.052252. Mixed Precision Training — Paulius Micikevicius, Sharan Narang, Jonah Alben, Gregory Diamos, Erich Elsen, and others, 2017https://scholar.google.com/scholar?q=Mixed+Precision+Training3. Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference — Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew Howard, and others, 2018https://scholar.google.com/scholar?q=Quantization+and+Training+of+Neural+Networks+for+Efficient+Integer-Arithmetic-Only+Inference4. FP8 Formats for Deep Learning — Paulius Micikevicius, Dusan Stosic, Neil Burgess, Marius Cornea, Pradeep Dubey, and others, 2022https://scholar.google.com/scholar?q=FP8+Formats+for+Deep+Learning5. AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration — Ji Lin, Jiaming Tang, Haotian Tang, Shang Yang, Wei-Ming Chen, Wei-Chen Wang, Guangxuan Xiao, Chuang Gan, Song Han, and others, 2023https://scholar.google.com/scholar?q=AWQ%3A+Activation-aware+Weight+Quantization+for+LLM+Compression+and+Acceleration6. FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness — Tri Dao et al., 2022https://scholar.google.com/scholar?q=FlashAttention%3A+Fast+and+Memory-Efficient+Exact+Attention+with+IO-Awareness7. A Full-Stack Search Technique for Domain Optimized Deep Learning Accelerators — Dan Zhang et al., 2022https://scholar.google.com/scholar?q=A+Full-Stack+Search+Technique+for+Domain+Optimized+Deep+Learning+Accelerators8. A Compute-in-Memory Chip Based on Resistive Random-Access Memory — Weier Wan et al., 2022https://scholar.google.com/scholar?q=A+Compute-in-Memory+Chip+Based+on+Resistive+Random-Access+Memory9. Intelligence per Watt: Measuring Intelligence Efficiency of Local AI — Jon Saad-Falcon et al., 2025https://scholar.google.com/scholar?q=Intelligence+per+Watt%3A+Measuring+Intelligence+Efficiency+of+Local+AI10. PinDrop: Breaking the Silence on SDCs in a Large-Scale Fleet — Peter W. Deutsch et al., 2026https://scholar.google.com/scholar?q=PinDrop%3A+Breaking+the+Silence+on+SDCs+in+a+Large-Scale+Fleet11. LMCache: An Efficient KV Cache Layer for Enterprise-Scale LLM Inference — Yuhan Liu et al., 2025https://scholar.google.com/scholar?q=LMCache%3A+An+Efficient+KV+Cache+Layer+for+Enterprise-Scale+LLM+Inference12. TraCT: Disaggregated LLM Serving with CXL Shared Memory KV Cache at Rack-Scale — Dongha Yoon et al., 2025https://scholar.google.com/scholar?q=TraCT%3A+Disaggregated+LLM+Serving+with+CXL+Shared+Memory+KV+Cache+at+Rack-Scale13. Inference without Interference: Disaggregate LLM Inference for Mixed Downstream Workloads — Cunchen Hu et al., 2024https://scholar.google.com/scholar?q=Inference+without+Interference%3A+Disaggregate+LLM+Inference+for+Mixed+Downstream+Workloads14. CXL-SpecKV: A Disaggregated FPGA Speculative KV-Cache for Datacenter LLM Serving — Dong Liu and Yanxuan Yu, 2025https://scholar.google.com/scholar?q=CXL-SpecKV%3A+A+Disaggregated+FPGA+Speculative+KV-Cache+for+Datacenter+LLM+Serving15. KVLink: Accelerating Large Language Models via Efficient KV Cache Reuse — Jingbo Yang et al., 2025https://scholar.google.com/scholar?q=KVLink%3A+Accelerating+Large+Language+Models+via+Efficient+KV+Cache+Reuse16. Selective KV-Cache Sharing to Mitigate Timing Side-Channels in LLM Inference — Kexin Chu et al., 2025https://scholar.google.com/scholar?q=Selective+KV-Cache+Sharing+to+Mitigate+Timing+Side-Channels+in+LLM+Inference17. Test-Time Training on Nearest Neighbors for Large Language Models — Moritz Hardt and Yu Sun, 2023https://scholar.google.com/scholar?q=Test-Time+Training+on+Nearest+Neighbors+for+Large+Language+Models18. Test-Time Learning for Large Language Models — Jinwu Hu et al., 2025https://scholar.google.com/scholar?q=Test-Time+Learning+for+Large+Language+Models19. AI Post Transformers: FlatAttention for Tile-Based Accelerator Inference — Hal Turing & Dr. Ada Shannon, 2026https://podcast.do-not-panic.com/episodes/2026-04-04-flatattention-for-tile-based-accelerator-56e6ca.mp320. AI Post Transformers: LPU Chip for Low-Latency LLM Inference — Hal Turing & Dr. Ada Shannon, 2026https://podcast.do-not-panic.com/episodes/2026-05-20-lpu-chip-for-low-latency-llm-inference-be13c3.mp321. AI Post Transformers: Mamba-3 for Efficient Sequence Modeling — Hal Turing & Dr. Ada Shannon, 2026https://podcast.do-not-panic.com/episodes/2026-04-16-mamba-3-for-efficient-sequence-modeling-97a22a.mp322. AI Post Transformers: Mistral 7B: Superior Performance in a Smaller Package — Hal Turing & Dr. Ada Shannon, 2025https://podcast.do-not-panic.com/episodes/mistral-7b-superior-performance-in-a-smaller-package/23. AI Post Transformers: PALOMA: Benchmarking Language Model Fit Across Domains — Hal Turing & Dr. Ada Shannon, 2026https://podcast.do-not-panic.com/episodes/2026-06-23-paloma-benchmarking-language-model-fit-a-360060.mp324. AI Post Transformers: Automating CNN Mapping on Embedded FPGAs — Hal Turing & Dr. Ada Shannon, 2026https://podcast.do-not-panic.com/episodes/2026-05-06-automating-cnn-mapping-on-embedded-fpgas-4c1dc3.mp325. AI Post Transformers: FPGA Neural Network Accelerators for Space — Hal Turing & Dr. Ada Shannon, 2026https://podcast.do-not-panic.com/episodes/2026-04-26-fpga-neural-network-accelerators-for-spa-3087ae.mp326. AI Post Transformers: ASI-Evolve for Data, Architectures, and RL — Hal Turing & Dr. Ada Shannon, 2026https://podcast.do-not-panic.com/episodes/2026-04-05-asi-evolve-for-data-architectures-and-rl-197b2b.mp313 min
- Efficient Post-Training Quantization with FP8This episode explores how post-training quantization can convert already-trained models into 8-bit floating point formats for cheaper inference, and why FP8 may outperform the older INT8 approach on modern transformers, LLMs, and diffusion models. It explains the tradeoff between exponent range and mantissa precision across FP8 formats such as E4M3, E5M2, and E3M4, with particular attention to how FP8 handles activation outliers and dynamic range more gracefully than fixed-scale INT8. The discussion centers on a hardware-aware deployment recipe, including which operators can stay quantized, where higher-precision accumulation still matters, and how BatchNorm recalibration helps low-precision inference match full-precision behavior. Listeners get a concrete result: across 75 architectures and more than 200 task cases, the paper reports 92.64% workload coverage for FP8 versus 65.87% for INT8, with E4M3 looking strongest for NLP while E3M4 is slightly better for some vision workloads.Sources:1. Efficient Post-training Quantization with FP8 Formats — Haihao Shen, Naveen Mellempudi, Xin He, Qun Gao, Chang Wang, Mengni Wang, 2023http://arxiv.org/abs/2309.145922. Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference — Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, et al., 2018https://scholar.google.com/scholar?q=Quantization+and+Training+of+Neural+Networks+for+Efficient+Integer-Arithmetic-Only+Inference3. A White Paper on Neural Network Quantization — Markus Nagel, Marios Fournarakis, Rana Ali Amjad, Yelysei Bondarenko, Mart van Baalen, Tijmen Blankevoort, 2021https://scholar.google.com/scholar?q=A+White+Paper+on+Neural+Network+Quantization4. LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale — Tim Dettmers, Mike Lewis, Younes Belkada, Luke Zettlemoyer, 2022https://scholar.google.com/scholar?q=LLM.int8%28%29%3A+8-bit+Matrix+Multiplication+for+Transformers+at+Scale5. SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models — Guangxuan Xiao, Ji Lin, Mickael Seznec, Hao Wu, Julien Demouth, Song Han, 2022https://scholar.google.com/scholar?q=SmoothQuant%3A+Accurate+and+Efficient+Post-Training+Quantization+for+Large+Language+Models6. 8-bit Numerical Formats for Deep Neural Networks — Badreddine Noune, Philip Jones, Daniel Justus, Dominic Masters, Carlo Luschi, 2022https://scholar.google.com/scholar?q=8-bit+Numerical+Formats+for+Deep+Neural+Networks7. FP8 Formats for Deep Learning — Paulius Micikevicius, Dusan Stosic, Neil Burgess, Marius Cornea, Pradeep Dubey, et al., 2022https://scholar.google.com/scholar?q=FP8+Formats+for+Deep+Learning8. FP8 Quantization: The Power of the Exponent — Andrey Kuzmin, Mart van Baalen, Yuwei Ren, Markus Nagel, Jorn Peters, Tijmen Blankevoort, 2022https://scholar.google.com/scholar?q=FP8+Quantization%3A+The+Power+of+the+Exponent9. Efficient Post-training Quantization with FP8 Formats — Haihao Shen, Naveen Mellempudi, Xin He, Qun Gao, Chang Wang, Mengni Wang, 2024https://scholar.google.com/scholar?q=Efficient+Post-training+Quantization+with+FP8+Formats10. Hybrid 8-bit Floating Point (HFP8) Training and Inference for Deep Neural Networks — Xiaohu Sun et al., 2019https://scholar.google.com/scholar?q=Hybrid+8-bit+Floating+Point+%28HFP8%29+Training+and+Inference+for+Deep+Neural+Networks11. Outlier Suppression: Pushing the Limit of Low-bit Transformer Language Models — Xiuying Wei et al., 2022https://scholar.google.com/scholar?q=Outlier+Suppression%3A+Pushing+the+Limit+of+Low-bit+Transformer+Language+Models12. Quantizable transformers: Removing outliers by helping attention heads do nothing — Bondarenko et al. (approx.), 2023?https://scholar.google.com/scholar?q=Quantizable+transformers%3A+Removing+outliers+by+helping+attention+heads+do+nothing13. Understanding and minimising outlier features in transformer training — author list not verified from provided snippet, 2024?https://scholar.google.com/scholar?q=Understanding+and+minimising+outlier+features+in+transformer+training14. QuanTool: A Benchmarking Framework for Evaluating Post-Training Quantization with Best Practices for Transformer Models — author list not verified from provided snippet, 2024?https://scholar.google.com/scholar?q=QuanTool%3A+A+Benchmarking+Framework+for+Evaluating+Post-Training+Quantization+with+Best+Practices+for+Transformer+Models15. GO-ViT: Fully Quantizing Vision Transformers by Grouping Outlier Channels — author list not verified from provided snippet, 2024?https://scholar.google.com/scholar?q=GO-ViT%3A+Fully+Quantizing+Vision+Transformers+by+Grouping+Outlier+Channels16. Understanding int4 quantization for language models: latency speedup, composability, and failure cases — author list not verified from provided snippet, 2024?https://scholar.google.com/scholar?q=Understanding+int4+quantization+for+language+models%3A+latency+speedup%2C+composability%2C+and+failure+cases17. Kvquant: Towards 10 million context length llm inference with kv cache quantization — author list not verified from provided snippet, 2024?https://scholar.google.com/scholar?q=Kvquant%3A+Towards+10+million+context+length+llm+inference+with+kv+cache+quantization18. AI Post Transformers: MIOpen and AMD's Open Deep Learning Primitives — Hal Turing & Dr. Ada Shannon, 2026https://podcast.do-not-panic.com/episodes/2026-06-23-miopen-and-amds-open-deep-learning-primi-052f82.mp319. AI Post Transformers: Deep Kernel Fusion for Transformer Decoding — Hal Turing & Dr. Ada Shannon, 2026https://podcast.do-not-panic.com/episodes/2026-05-15-deep-kernel-fusion-for-transformer-decod-b1a703.mp320. AI Post Transformers: Mooncake for KV Cache-Centric LLM Serving — Hal Turing & Dr. Ada Shannon, 2026https://podcast.do-not-panic.com/episodes/2026-06-05-mooncake-for-kv-cache-centric-llm-servin-1086d0.mp3Interactive Visualization: Efficient Post-Training Quantization with FP814 min
- PALOMA: Benchmarking Language Model Fit Across DomainsThis episode explores PALOMA, a NeurIPS 2024 benchmark designed to measure how well language models fit many different language distributions instead of relying on a single average perplexity score. It explains why one global loss number can hide important weaknesses across domains such as specific subreddits, scientific writing, or programming languages, and highlights PALOMA’s fine-grained setup across 546 English and code domains from 16 sources. The discussion places PALOMA in context with earlier language-model evaluation traditions, scaling-law work, and broader benchmark efforts like HELM, while arguing that evaluation design determines what claims researchers can actually make. Listeners would find it interesting for its clear case that better measurement, data curation, and decontamination can reveal model behavior that broad headline metrics often miss.Sources:1. Paloma: A Benchmark for Evaluating Language Model Fit — Ian Magnusson, Akshita Bhagia, Valentin Hofmann, Luca Soldaini, Ananya Harsh Jha, Oyvind Tafjord, Dustin Schwenk, Evan Pete Walsh, Yanai Elazar, Kyle Lo, Dirk Groeneveld, Iz Beltagy, Hannaneh Hajishirzi, Noah A. Smith, Kyle Richardson, Jesse Dodge, 2023http://arxiv.org/abs/2312.105232. One Billion Word Benchmark for Measuring Progress in Statistical Language Modeling — Ciprian Chelba, Tomas Mikolov, Mike Schuster, Qi Ge, Thorsten Brants, et al., 2013https://scholar.google.com/scholar?q=One+Billion+Word+Benchmark+for+Measuring+Progress+in+Statistical+Language+Modeling3. Scaling Laws for Neural Language Models — Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Dario Amodei, et al., 2020https://scholar.google.com/scholar?q=Scaling+Laws+for+Neural+Language+Models4. Training Compute-Optimal Large Language Models — Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Jack W. Rae, Oriol Vinyals, Laurent Sifre, et al., 2022https://scholar.google.com/scholar?q=Training+Compute-Optimal+Large+Language+Models5. Paloma: A Benchmark for Evaluating Language Model Fit — Ian Magnusson, Akshita Bhagia, Valentin Hofmann, Luca Soldaini, Kyle Richardson, Jesse Dodge, et al., 2024https://scholar.google.com/scholar?q=Paloma%3A+A+Benchmark+for+Evaluating+Language+Model+Fit6. M2D2: A Massively Multi-domain Language Modeling Dataset — Machel Reid, Victor Zhong, Suchin Gururangan, Luke Zettlemoyer, 2022https://scholar.google.com/scholar?q=M2D2%3A+A+Massively+Multi-domain+Language+Modeling+Dataset7. Holistic Evaluation of Language Models — Percy Liang et al., 2022https://scholar.google.com/scholar?q=Holistic+Evaluation+of+Language+Models8. Same Pre-training Loss, Better Downstream: Implicit Bias Matters for Language Models — Hong Liu, Sang Michael Xie, Zhiyuan Li, Tengyu Ma, 2022https://scholar.google.com/scholar?q=Same+Pre-training+Loss%2C+Better+Downstream%3A+Implicit+Bias+Matters+for+Language+Models9. Language Model Evaluation Beyond Perplexity — Clara Meister, Ryan Cotterell, 2021https://scholar.google.com/scholar?q=Language+Model+Evaluation+Beyond+Perplexity10. Unsupervised Domain Clusters in Pretrained Language Models — Roee Aharoni, Yoav Goldberg, 2020https://scholar.google.com/scholar?q=Unsupervised+Domain+Clusters+in+Pretrained+Language+Models11. DataComp-LM: In search of the next generation of training sets for language models — Jeffrey Li et al., 2024https://scholar.google.com/scholar?q=DataComp-LM%3A+In+search+of+the+next+generation+of+training+sets+for+language+models12. Rethinking Perplexity: Revealing the Impact of Input Length on Perplexity Evaluation in LLMs — Letian Cheng et al., 2026https://scholar.google.com/scholar?q=Rethinking+Perplexity%3A+Revealing+the+Impact+of+Input+Length+on+Perplexity+Evaluation+in+LLMs13. Rethinking Benchmark and Contamination for Language Models with Rephrased Samples — Shuo Yang et al., 2023https://scholar.google.com/scholar?q=Rethinking+Benchmark+and+Contamination+for+Language+Models+with+Rephrased+Samples14. PaCoST: Paired Confidence Significance Testing for Benchmark Contamination Detection in Large Language Models — Huixuan Zhang et al., 2024https://scholar.google.com/scholar?q=PaCoST%3A+Paired+Confidence+Significance+Testing+for+Benchmark+Contamination+Detection+in+Large+Language+Models15. RegMix: Data Mixture as Regression for Language Model Pre-training — Qian Liu et al., 2024https://scholar.google.com/scholar?q=RegMix%3A+Data+Mixture+as+Regression+for+Language+Model+Pre-training16. Data Mixing Laws: Optimizing Data Mixtures by Predicting Language Modeling Performance — Jiasheng Ye et al., 2024https://scholar.google.com/scholar?q=Data+Mixing+Laws%3A+Optimizing+Data+Mixtures+by+Predicting+Language+Modeling+Performance17. The Fools are Certain; the Wise are Doubtful: Exploring LLM Confidence in Code Completion — Zoe Kotti et al., 2025https://scholar.google.com/scholar?q=The+Fools+are+Certain%3B+the+Wise+are+Doubtful%3A+Exploring+LLM+Confidence+in+Code+Completion18. AI Post Transformers: Model-Aware Tokenizer Transfer for Multilingual LLMs — Hal Turing & Dr. Ada Shannon, 2026https://podcast.do-not-panic.com/episodes/2026-03-16-model-aware-tokenizer-transfer-for-multi-90666c.mp319. AI Post Transformers: LLM Benchmark Robustness to Linguistic Variation — Hal Turing & Dr. Ada Shannon, 2025https://podcast.do-not-panic.com/episodes/llm-benchmark-robustness-to-linguistic-variation/15 min
- MIOpen and AMD's Open Deep Learning PrimitivesThis episode explores AMD’s open-source MIOpen library and why deep learning primitives such as convolution, pooling, normalization, and activations are the layer where model performance meets GPU hardware reality. It explains how CNN throughput depends on low-level execution choices, comparing approaches such as im2col-plus-GEMM and Winograd convolution, and shows why libraries like MIOpen need solver-based algorithm selection and auto-tuning to match different workload shapes, precisions, and GPUs. The discussion also covers mixed-precision support, especially bfloat16, along with kernel fusion and composable kernels as ways to reduce memory traffic and launch overhead while keeping vendor-library speed. Listeners would find it interesting because it turns “invisible infrastructure” into a concrete systems story about how open-source GPU software can shape real model training and inference performance.Sources:1. MIOpen: An Open Source Library For Deep Learning Primitives — Jehandad Khan, Paul Fultz, Artem Tamazov, Daniel Lowell, Chao Liu, Michael Melesse, Murali Nandhimandalam, Kamil Nasyrov, Ilya Perminov, Tejash Shah, Vasilii Filippov, Jing Zhang, Jing Zhou, Bragadeesh Natarajan, Mayank Daga, 2019http://arxiv.org/abs/1910.000782. cuDNN: Efficient Primitives for Deep Learning — Sharan Chetlur, Cliff Woolley, Philippe Vandermersch, Jonathan Cohen, John Tran, Bryan Catanzaro, Evan Shelhamer, 2014https://scholar.google.com/scholar?q=cuDNN%3A+Efficient+Primitives+for+Deep+Learning3. MIOpen: An Open Source Library For Deep Learning Primitives — Jehandad Khan, Paul Fultz, Artem Tamazov, Daniel Lowell, Chao Liu, Michael Melesse, Murali Nandhimandalam, Kamil Nasyrov, Ilya Perminov, Tejash Shah, Vasilii Filippov, Jing Zhang, Jing Zhou, Bragadeesh Natarajan, Mayank Daga, 2019https://scholar.google.com/scholar?q=MIOpen%3A+An+Open+Source+Library+For+Deep+Learning+Primitives4. TVM: An Automated End-to-End Optimizing Compiler for Deep Learning — Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan, Meghan Cowan, Haichen Shen, Leyuan Wang, Yuwei Hu, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy, 2018https://scholar.google.com/scholar?q=TVM%3A+An+Automated+End-to-End+Optimizing+Compiler+for+Deep+Learning5. Ansor: Generating High-Performance Tensor Programs for Deep Learning — Lianmin Zheng, Chengfan Jia, Minmin Sun, Zhao Wu, Cody Hao Yu, Ameer Haj-Ali, Yida Wang, Jun Yang, Danyang Zhuo, Koushik Sen, Joseph E. Gonzalez, Ion Stoica, 2020https://scholar.google.com/scholar?q=Ansor%3A+Generating+High-Performance+Tensor+Programs+for+Deep+Learning6. Automatically tuned linear algebra software — R. Clint Whaley and Jack J. Dongarra, 1998https://scholar.google.com/scholar?q=Automatically+tuned+linear+algebra+software7. Fast Algorithms for Convolutional Neural Networks — Andrew Lavin and Scott Gray, 2015https://scholar.google.com/scholar?q=Fast+Algorithms+for+Convolutional+Neural+Networks8. TVM: end-to-end optimization stack for deep learning — Tianqi Chen et al., 2018https://scholar.google.com/scholar?q=TVM%3A+end-to-end+optimization+stack+for+deep+learning9. Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions — Nicolas Vasilache et al., 2018https://scholar.google.com/scholar?q=Tensor+Comprehensions%3A+Framework-Agnostic+High-Performance+Machine+Learning+Abstractions10. oneDNN Graph Compiler: A Hybrid Approach for High-Performance Deep Learning Compilation — approx. Intel oneDNN graph compiler authors, 2023https://scholar.google.com/scholar?q=oneDNN+Graph+Compiler%3A+A+Hybrid+Approach+for+High-Performance+Deep+Learning+Compilation11. SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning — approx. TVM / SparseTIR authors, 2023https://scholar.google.com/scholar?q=SparseTIR%3A+Composable+Abstractions+for+Sparse+Compilation+in+Deep+Learning12. Autotuning Convolutions Is Easier Than You Think — approx. tensor-compiler autotuning authors, 2023https://scholar.google.com/scholar?q=Autotuning+Convolutions+Is+Easier+Than+You+Think13. Haotuner: A Hardware Adaptive Operator Auto-Tuner for Dynamic Shape Tensor Compilers — approx. Haotuner authors, 2024https://scholar.google.com/scholar?q=Haotuner%3A+A+Hardware+Adaptive+Operator+Auto-Tuner+for+Dynamic+Shape+Tensor+Compilers14. The Case for Training Large Models in Low Precision — approx. low-precision training authors, 2024https://scholar.google.com/scholar?q=The+Case+for+Training+Large+Models+in+Low+Precision15. AI Post Transformers: Deep Kernel Fusion for Transformer Decoding — Hal Turing & Dr. Ada Shannon, 2026https://podcast.do-not-panic.com/episodes/2026-05-15-deep-kernel-fusion-for-transformer-decod-b1a703.mp316. AI Post Transformers: FlashFuser and Hopper-Era FFN Kernel Fusion — Hal Turing & Dr. Ada Shannon, 2026https://podcast.do-not-panic.com/episodes/2026-05-15-flashfuser-and-hopper-era-ffn-kernel-fus-e1fce9.mp317. AI Post Transformers: Automating DNN Compilation for FPGA Accelerators — Hal Turing & Dr. Ada Shannon, 2026https://podcast.do-not-panic.com/episodes/2026-05-06-automating-dnn-compilation-for-fpga-acce-6ef9bf.mp318. AI Post Transformers: Caffeine: A Unified FPGA for CNNs — Hal Turing & Dr. Ada Shannon, 2026https://podcast.do-not-panic.com/episodes/2026-05-06-caffeine-a-unified-fpga-for-cnns-e8acbe.mp314 min
- Why Open Relational Foundation Models FailThis episode explores why open relational foundation models struggle on real downstream database tasks, using OpenRFM as a case study in relational in-context learning across multi-table data such as healthcare, fraud, and recommendation systems. It explains how the RT backbone builds breadth-first relational contexts, why that setup often reduces to a kernel-regression-like similarity lookup, and how limited labeled evidence in those walks creates a label-scarcity bottleneck. The discussion highlights the paper’s main argument that both architecture and pretraining prior matter: adding a TabICL head gives the query direct access to a full batch of support examples, while better synthetic and real-data pretraining pushes the model from shallow similarity matching toward actual relational feature learning. Listeners would find it interesting because the episode goes beyond benchmark gains to unpack a concrete failure mode, then shows how OpenRFM turns that diagnosis into a reported roughly 30% average improvement over the RT baseline.Sources:1. OpenRFM: Dissecting Relational In-Context Learning — Zhikai Chen, Junyu Yin, Jialiang Gu, Siheng Xiong, Xiaoze Liu, Ruowang Zhang, Keren Zhou, Kai Guo, 2026http://arxiv.org/abs/2606.043202. Neural Tangent Kernel: Convergence and Generalization in Neural Networks — Arthur Jacot, Franck Gabriel, Clement Hongler, 2018https://arxiv.org/abs/1806.075723. On Lazy Training in Differentiable Programming — Lenaic Chizat, Edouard Oyallon, Francis Bach, 2019https://arxiv.org/abs/1812.079564. What learning algorithm is in-context learning? Investigations with linear models — Ekin Akyurek, Dale Schuurmans, Jacob Andreas, Tengyu Ma, Denny Zhou, 2022https://arxiv.org/abs/2211.156615. TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second — Noah Hollmann, Samuel Muller, Katharina Eggensperger, Frank Hutter, 2022https://arxiv.org/abs/2207.018486. Assortative mixing in networks — M. E. J. Newman, 2002https://arxiv.org/abs/cond-mat/02054057. Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs — Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, Danai Koutra, 2020https://arxiv.org/abs/2006.114688. Graph Neural Networks with Heterophily — Jiong Zhu, Ryan A. Rossi, Anup Rao, Tung Mai, Nedim Lipka, Nesreen K. Ahmed, Danai Koutra, 2020https://arxiv.org/abs/2009.135669. PluRel: Synthetic Data unlocks Scaling Laws for Relational Foundation Models — Vignesh Kothapalli, Rishabh Ranjan, Valter Hudovernik, Vijay Prakash Dwivedi, Johannes Hoffart, Carlos Guestrin, Jure Leskovec, 2026https://arxiv.org/abs/2602.0402910. Relational Transformer: Toward Zero-Shot Foundation Models for Relational Data — Rishabh Ranjan, Valter Hudovernik, Mark Znidar, Charilaos I. Kanatsoulis, Roshan Reddy Upendra, Mahmoud Mohammadi, Joe Meyer, Tom Palczewski, Carlos Guestrin, and Jure Leskovec, 2026https://scholar.google.com/scholar?q=Relational+Transformer%3A+Toward+Zero-Shot+Foundation+Models+for+Relational+Data11. TabICL: A Tabular Foundation Model for In-Context Learning on Large Data — Jingang Qu, David Holzmuller, Gael Varoquaux, and Marine Le Morvan, 2025https://scholar.google.com/scholar?q=TabICL%3A+A+Tabular+Foundation+Model+for+In-Context+Learning+on+Large+Data12. KumoRFM: A Foundation Model for In-Context Learning on Relational Data — Matthias Fey, Vid Kocijan, Federico Lopez, Jan Eric Lenssen, and Jure Leskovec, 2025https://scholar.google.com/scholar?q=KumoRFM%3A+A+Foundation+Model+for+In-Context+Learning+on+Relational+Data13. RelBench: A Benchmark for Deep Learning on Relational Databases — Joshua Robinson, Rishabh Ranjan, Weihua Hu, Kexin Huang, Jiaqi Han, Alejandro Dobles, Matthias Fey, Jan E. Lenssen, Yiwen Yuan, Zecheng Zhang, Xinwei He, and Jure Leskovec, 2024https://scholar.google.com/scholar?q=RelBench%3A+A+Benchmark+for+Deep+Learning+on+Relational+Databases14. Understanding Emergent In-Context Learning from a Kernel Regression Perspective — Chi Han, Ziqi Wang, Han Zhao, and Heng Ji, 2025https://scholar.google.com/scholar?q=Understanding+Emergent+In-Context+Learning+from+a+Kernel+Regression+Perspective15. Retrieval & Fine-Tuning for In-Context Tabular Models — Valentin Thomas et al., 2024https://scholar.google.com/scholar?q=Retrieval+%26+Fine-Tuning+for+In-Context+Tabular+Models16. On Finetuning Tabular Foundation Models — Ivan Rubachev et al., 2025https://scholar.google.com/scholar?q=On+Finetuning+Tabular+Foundation+Models17. Turning Tabular Foundation Models into Graph Foundation Models — Dmitry Eremeev et al., 2025https://scholar.google.com/scholar?q=Turning+Tabular+Foundation+Models+into+Graph+Foundation+Models18. Of Graphs and Tables: Zero-Shot Node Classification with Tabular Foundation Models — Adrian Hayler et al., 2025https://scholar.google.com/scholar?q=Of+Graphs+and+Tables%3A+Zero-Shot+Node+Classification+with+Tabular+Foundation+Models19. A Pre-training Framework for Relational Data with Information-theoretic Principles — Quang Truong et al., 2025https://scholar.google.com/scholar?q=A+Pre-training+Framework+for+Relational+Data+with+Information-theoretic+Principles20. When Heterophily Meets Heterogeneity: New Graph Benchmarks and Effective Methods — Junhong Lin et al., 2024https://scholar.google.com/scholar?q=When+Heterophily+Meets+Heterogeneity%3A+New+Graph+Benchmarks+and+Effective+Methods21. Aligning the Spectrum: Hybrid Graph Pre-training and Prompt Tuning across Homophily and Heterophily — Haitong Luo et al., 2025https://scholar.google.com/scholar?q=Aligning+the+Spectrum%3A+Hybrid+Graph+Pre-training+and+Prompt+Tuning+across+Homophily+and+Heterophily22. AI Post Transformers: Can Models Learn from Long Context? — Hal Turing & Dr. Ada Shannon, 2026https://podcast.do-not-panic.com/episodes/2026-05-05-can-models-learn-from-long-context-77533e.mp323. AI Post Transformers: When Many-Shot CoT Becomes Test-Time Learning — Hal Turing & Dr. Ada Shannon, 2026https://podcast.do-not-panic.com/episodes/2026-05-15-when-many-shot-cot-becomes-test-time-lea-c25bfe.mp324. AI Post Transformers: TransactionGPT as a Payments Foundation Model — Hal Turing & Dr. Ada Shannon, 2026https://podcast.do-not-panic.com/episodes/2026-06-17-transactiongpt-as-a-payments-foundation-d53687.mp311 min
- X-LLM: Treating Multimodalities as Foreign LanguagesThis episode explores X-LLM, a 2023 system that treats images, video, and speech as foreign languages a frozen ChatGLM can learn to read through learned modality-to-language bridges. It breaks down the paper’s architecture, including Q-Former-based visual adapters and a separate speech pipeline with continuous integrate-and-fire modules, to show how three sensory routes feed a single dialogue model instead of one end-to-end multimodal transformer. The discussion argues that X-LLM mattered less as proof of a universal multimodal theory than as a practical open-model recipe shaped by 2023 compute limits, with its Chinese-language backbone playing a real methodological role rather than serving as background context. Listeners get a sharp comparison between this bridge-based approach and later end-to-end systems such as GPT-4o and Gemini 1.5, making the episode useful for understanding how modern multimodal assistants actually evolved.Sources:1. X-LLM: Bootstrapping Advanced Large Language Models by Treating Multi-Modalities as Foreign Languages — Feilong Chen, Minglun Han, Haozhi Zhao, Qingyang Zhang, Jing Shi, Shuang Xu, Bo Xu, 2023http://arxiv.org/abs/2305.041602. Multimodal Few-Shot Learning with Frozen Language Models — Maria Tsimpoukelli, Jacob Menick, Oriol Vinyals, Felix Hill, 2021https://scholar.google.com/scholar?q=Multimodal+Few-Shot+Learning+with+Frozen+Language+Models3. Flamingo: a Visual Language Model for Few-Shot Learning — Jean-Baptiste Alayrac, Jeff Donahue, Karen Simonyan, Oriol Vinyals, Andrew Zisserman, 2022https://scholar.google.com/scholar?q=Flamingo%3A+a+Visual+Language+Model+for+Few-Shot+Learning4. BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models — Junnan Li, Dongxu Li, Silvio Savarese, Steven Hoi, 2023https://scholar.google.com/scholar?q=BLIP-2%3A+Bootstrapping+Language-Image+Pre-training+with+Frozen+Image+Encoders+and+Large+Language+Models5. SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities — Dong Zhang, Shimin Li, Xin Zhang, Xipeng Qiu, 2023https://scholar.google.com/scholar?q=SpeechGPT%3A+Empowering+Large+Language+Models+with+Intrinsic+Cross-Modal+Conversational+Abilities6. Visual Instruction Tuning — Haotian Liu, Chunyuan Li, Qingyang Wu, Yong Jae Lee, 2023https://scholar.google.com/scholar?q=Visual+Instruction+Tuning7. MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models — Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, Mohamed Elhoseiny, 2023https://scholar.google.com/scholar?q=MiniGPT-4%3A+Enhancing+Vision-Language+Understanding+with+Advanced+Large+Language+Models8. PaLM-E: An Embodied Multimodal Language Model — Danny Driess et al., 2023https://scholar.google.com/scholar?q=PaLM-E%3A+An+Embodied+Multimodal+Language+Model9. CIF: Continuous Integrate-and-Fire for End-to-End Speech Recognition — Linhao Dong, Bo Xu, 2019https://scholar.google.com/scholar?q=CIF%3A+Continuous+Integrate-and-Fire+for+End-to-End+Speech+Recognition10. VL-JEPA: Joint Embedding Predictive Architecture for Vision-language — Delong Chen et al., 2025https://scholar.google.com/scholar?q=VL-JEPA%3A+Joint+Embedding+Predictive+Architecture+for+Vision-language11. TokenPacker: Efficient Visual Projector for Multimodal LLM — Wentong Li et al., 2024https://scholar.google.com/scholar?q=TokenPacker%3A+Efficient+Visual+Projector+for+Multimodal+LLM12. Slot-MLLM: Object-Centric Visual Tokenization for Multimodal LLM — Donghwan Chi et al., 2025https://scholar.google.com/scholar?q=Slot-MLLM%3A+Object-Centric+Visual+Tokenization+for+Multimodal+LLM13. Auto-Encoding Morph-Tokens for Multimodal LLM — Kaihang Pan et al., 2024https://scholar.google.com/scholar?q=Auto-Encoding+Morph-Tokens+for+Multimodal+LLM14. ViCA: Efficient Multimodal LLMs with Vision-Only Cross-Attention — Wenjie Liu et al., 2026https://scholar.google.com/scholar?q=ViCA%3A+Efficient+Multimodal+LLMs+with+Vision-Only+Cross-Attention15. F-LMM: Grounding Frozen Large Multimodal Models — Size Wu et al., 2024https://scholar.google.com/scholar?q=F-LMM%3A+Grounding+Frozen+Large+Multimodal+Models16. MultiModal-GPT: A Vision and Language Model for Dialogue with Humans — Tao Gong et al., 2023https://scholar.google.com/scholar?q=MultiModal-GPT%3A+A+Vision+and+Language+Model+for+Dialogue+with+Humans17. AI Post Transformers: UniVideo: Unified Video Understanding, Generation, and Editing — Hal Turing & Dr. Ada Shannon, Sat,https://podcast.do-not-panic.com/episodes/univideo-unified-video-understanding-generation-and-editing/18. AI Post Transformers: DeepSeek-OCR: Contexts Optical Compression — Hal Turing & Dr. Ada Shannon, Sat,https://podcast.do-not-panic.com/episodes/deepseek-ocr-contexts-optical-compression/Interactive Visualization: X-LLM: Treating Multimodalities as Foreign Languages0 min
- Simulating Individuals with Self-Reported LLM AgentsThis episode explores a paper on building reusable LLM-based simulations of specific individuals by grounding agents in people’s own interviews, survey responses, or both, rather than relying on thin demographic personas. It explains how the system was tested on 1,052 Americans using holdout evaluations across survey questions, personality traits, behavioral experiments, and randomized intervention outcomes to measure real generalization instead of simple recall. The discussion highlights the main result that self-report-grounded agents performed much better than demographics-only baselines, with combined interview-and-survey agents reaching 86 percent of a person’s own two-week consistency versus 74 percent for demographics alone. It is interesting because it frames these agents as a possible new tool for social science and policy research while also probing hard questions about fairness, stereotype reduction, and whether strong results on language-based self-reports truly amount to deep behavior simulation.Sources:1. LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of Individuals — Joon Sung Park, Carolyn Q. Zou, Jonne Kamphorst, Niles Egan, Aaron Shaw, Benjamin Mako Hill, Carrie Cai, Meredith Ringel Morris, Percy Liang, Robb Willer, Michael S. Bernstein, 2024http://arxiv.org/abs/2411.101092. Generative Agents: Interactive Simulacra of Human Behavior — Joon Sung Park, Joseph C. O'Brien, Carrie J. Cai, Meredith Ringel Morris, Percy Liang, Michael S. Bernstein, 2023https://arxiv.org/abs/2304.034423. Out of One, Many: Using Language Models to Simulate Human Samples — Lisa P. Argyle, Ethan C. Busby, Nancy Fulda, Joshua Gubler, Christopher Rytting, David Wingate, 2022https://arxiv.org/abs/2209.068994. Generative Agent Simulations of 1,000 People — Joon Sung Park, Carolyn Q. Zou, Aaron Shaw, Benjamin Mako Hill, Carrie Cai, Meredith Ringel Morris, Robb Willer, Percy Liang, Michael S. Bernstein, 2024https://arxiv.org/abs/2411.101095. AI-Augmented Surveys: Leveraging Large Language Models and Surveys for Opinion Prediction — Junsol Kim, Byungkyu Lee, 2023https://arxiv.org/abs/2305.096206. Large Language Models Show Human-like Social Desirability Biases in Survey Responses — Aadesh Salecha, Molly E. Ireland, Shashanka Subrahmanya, Joao Sedoc, Lyle H. Ungar, Johannes C. Eichstaedt, 2024https://arxiv.org/abs/2405.060587. Interview-Informed Generative Agents for Product Discovery: A Validation Study — Zichao Wang, Alexa Siu, 2026https://arxiv.org/abs/2603.298908. Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus? — John J. Horton, Apostolos Filippas, Benjamin S. Manning, 2023https://arxiv.org/abs/2301.075439. From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agents — Xinyi Mou, Xuanwen Ding, Qi He, Liang Wang, Jingcong Liang, Xinnong Zhang, et al., 2024https://arxiv.org/abs/2412.0356310. Using Large Language Models to Create AI Personas for Replication, Generalization and Prediction of Media Effects: An Empirical Test of 133 Published Experimental Research Findings — Leo Yeykelis, Kaavya Pichai, James J. Cummings, Byron Reeves, 2024https://arxiv.org/abs/2408.1607311. Synthetic Users, Real Differences: an Evaluation Framework for User Simulation in Multi-Turn Conversations — Yu Lu Liu, Hyokun Yun, Tanya Roosta, Ziang Xiao, 2026https://arxiv.org/abs/2605.0262412. Latent Human Traits in the Language of Social Media: An Open-Vocabulary Approach — Vivek Kulkarni, Margaret L. Kern, David Stillwell, Michal Kosinski, Sandra Matz, Lyle Ungar, Steven Skiena, H. Andrew Schwartz, 2017https://arxiv.org/abs/1705.0803813. Is ChatGPT a Good Personality Recognizer? A Preliminary Study — Yu Ji, Wen Wu, Hong Zheng, Yi Hu, Xi Chen, Liang He, 2023https://arxiv.org/abs/2307.0395214. Can LLMs Infer Personality from Real World Conversations? — Jianfeng Zhu, Ruoming Jin, Karin G. Coifman, 2025https://arxiv.org/abs/2507.1435515. The Personality Illusion: Revealing Dissociation Between Self-Reports & Behavior in LLMs — Pengrui Han, Rafal Kocielnik, Peiyang Song, Ramit Debnath, Dean Mobbs, Anima Anandkumar, R. Michael Alvarez, 2025https://arxiv.org/abs/2509.0373016. Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Models — Myra Cheng, Esin Durmus, Dan Jurafsky, 2023https://arxiv.org/abs/2305.1818917. On the steerability of large language models toward data-driven personas — Junyi Li, Ninareh Mehrabi, Charith Peris, Palash Goyal, Kai-Wei Chang, Aram Galstyan, Richard Zemel, Rahul Gupta, 2023https://arxiv.org/abs/2311.0497818. Reading Between the Prompts: How Stereotypes Shape LLM's Implicit Personalization — Vera Neplenbroek, Arianna Bisazza, Raquel Fernandez, 2025https://arxiv.org/abs/2505.1646719. Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies — Gati Aher, Rosa I. Arriaga, Adam Tauman Kalai, 2022https://scholar.google.com/scholar?q=Using+Large+Language+Models+to+Simulate+Multiple+Humans+and+Replicate+Human+Subject+Studies20. Can Large Language Models Capture Public Opinion about Global Warming? An Empirical Assessment of Algorithmic Fidelity and Bias — S. Lee, T. Q. Peng, M. H. Goldberg, S. A. Rosenthal, J. E. Kotcher, E. W. Maibach, A. Leiserowitz, 2023https://scholar.google.com/scholar?q=Can+Large+Language+Models+Capture+Public+Opinion+about+Global+Warming%3F+An+Empirical+Assessment+of+Algorithmic+Fidelity+and+Bias21. How Far are LLMs from Being Our Digital Twins? A Benchmark for Persona-Based Behavior Chain Simulation — Rui Li, Heming Xia, Xinfeng Yuan, Qingxiu Dong, Lei Sha, Wenjie Li, Zhifang Sui, 2025https://scholar.google.com/scholar?q=How+Far+are+LLMs+from+Being+Our+Digital+Twins%3F+A+Benchmark+for+Persona-Based+Behavior+Chain+Simulation22. Know Me, Respond to Me: Benchmarking LLMs for Dynamic User Profiling and Personalized Responses at Scale — Bowen Jiang et al., 2025https://scholar.google.com/scholar?q=Know+Me%2C+Respond+to+Me%3A+Benchmarking+LLMs+for+Dynamic+User+Profiling+and+Personalized+Responses+at+Scale23. PersonaX: A Recommendation Agent Oriented User Modeling Framework for Long Behavior Sequence — Yunxiao Shi et al., 2025https://scholar.google.com/scholar?q=PersonaX%3A+A+Recommendation+Agent+Oriented+User+Modeling+Framework+for+Long+Behavior+Sequence24. Finetuning LLMs for Human Behavior Prediction in Social Science Experiments — Akaash Kolluri, Shengguang Wu, Joon Sung Park, Michael S. Bernstein, 2025https://scholar.google.com/scholar?q=Finetuning+LLMs+for+Human+Behavior+Prediction+in+Social+Science+Experiments25. Tuning Language Models for Robust Prediction of Diverse User Behaviors — Fanjin Meng et al., 2025https://scholar.google.com/scholar?q=Tuning+Language+Models+for+Robust+Prediction+of+Diverse+User+Behaviors26. AI Post Transformers: RAGEN-2: Reasoning Collapse in Agentic RL — Hal Turing & Dr. Ada Shannon, 2026https://podcast.do-not-panic.com/episodes/2026-06-07-ragen-2-reasoning-collapse-in-agentic-rl-3cfa0b.mp327. AI Post Transformers: Split Personality Training Reveals Latent Knowledge — Hal Turing & Dr. Ada Shannon, 2026https://podcast.do-not-panic.com/episodes/2026-05-08-split-personality-training-reveals-laten-c84616.mp328. AI Post Transformers: End-to-End Context Compression at Scale — Hal Turing & Dr. Ada Shannon, 2026https://podcast.do-not-panic.com/episodes/2026-06-10-end-to-end-context-compression-at-scale-278c70.mp3Interactive Visualization: Simulating Individuals with Self-Reported LLM Agents11 min
- Building General User Models from Computer UseThis episode explores the UIST 2025 paper "Creating General User Models from Computer Use," which proposes building a persistent user model from raw computer traces such as screenshots, UI text, message context, and app switching. It explains how the system stores confidence-weighted natural-language propositions about a person’s preferences, knowledge, goals, and current situation, aiming to support cross-application assistants that can help proactively rather than waiting for explicit requests. The discussion situates the idea against earlier recommender systems, Bayesian user modeling, and newer LLM memory architectures, arguing that the paper is most interesting as a synthesis of HCI user modeling and retrieval-and-revision style AI memory. Listeners would find it compelling because it gets concrete about both the upside of more context-aware assistants and the hard problems underneath them, including noisy behavioral data, narrow evidence relative to broad claims, and the privacy risks of inferring things users never said aloud.Sources:1. Creating General User Models from Computer Use — Omar Shaikh, Shardul Sapkota, Shan Rizvi, Eric Horvitz, Joon Sung Park, Diyi Yang, Michael S. Bernstein, 2025http://arxiv.org/abs/2505.108312. User Modeling via Stereotypes — Elaine Rich, 1979https://scholar.google.com/scholar?q=User+Modeling+via+Stereotypes3. The Lumiere Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users — Eric J. Horvitz, John S. Breese, David Heckerman, David Hovel, Koos Rommelse, 1998https://scholar.google.com/scholar?q=The+Lumiere+Project%3A+Bayesian+User+Modeling+for+Inferring+the+Goals+and+Needs+of+Software+Users4. Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions — Gediminas Adomavicius, Alexander Tuzhilin, 2005https://scholar.google.com/scholar?q=Toward+the+Next+Generation+of+Recommender+Systems%3A+A+Survey+of+the+State-of-the-Art+and+Possible+Extensions5. User Modeling and User Profiling: A Comprehensive Survey — Erasmo Purificato, Ludovico Boratto, Ernesto William De Luca, 2024https://scholar.google.com/scholar?q=User+Modeling+and+User+Profiling%3A+A+Comprehensive+Survey6. Generative Agents: Interactive Simulacra of Human Behavior — Joon Sung Park, Joseph C. O'Brien, Carrie J. Cai, Meredith Ringel Morris, Percy Liang, Michael S. Bernstein, 2023https://scholar.google.com/scholar?q=Generative+Agents%3A+Interactive+Simulacra+of+Human+Behavior7. MemGPT: Towards LLMs as Operating Systems — Charles Packer, Sarah Wooders, Kevin Lin, Vivian Fang, Shishir G. Patil, Ion Stoica, Joseph E. Gonzalez, 2023https://scholar.google.com/scholar?q=MemGPT%3A+Towards+LLMs+as+Operating+Systems8. Collaborative Gym: A Framework for Enabling and Evaluating Human-Agent Collaboration — Yijia Shao, Vinay Samuel, Yucheng Jiang, John Yang, Diyi Yang, 2024https://scholar.google.com/scholar?q=Collaborative+Gym%3A+A+Framework+for+Enabling+and+Evaluating+Human-Agent+Collaboration9. Supporting Physical Activity Behavior Change with LLM-Based Conversational Agents — Matthew Jörke, Shardul Sapkota, Lyndsea Warkenthien, Niklas Vainio, Paul Schmiedmayer, Emma Brunskill, James Landay, 2024https://scholar.google.com/scholar?q=Supporting+Physical+Activity+Behavior+Change+with+LLM-Based+Conversational+Agents10. TaskTracer: a desktop environment to support multi-tasking knowledge workers — Anton N. Dragunov, Thomas G. Dietterich, Kevin Johnsrude, Matthew McLaughlin, Lida Li, Jonathan L. Herlocker, 2005https://scholar.google.com/scholar?q=TaskTracer%3A+a+desktop+environment+to+support+multi-tasking+knowledge+workers11. UI-TARS: Pioneering Automated GUI Interaction with Native Agents — Yujia Qin et al., 2025https://arxiv.org/abs/2501.1232612. Need Help? Designing Proactive AI Assistants for Programming — Valerie Chen et al., 2024/2025https://arxiv.org/abs/2410.0459613. Proactive Agent Research Environment: Simulating Active Users to Evaluate Proactive Assistants — Deepak Nathani et al., 2026https://arxiv.org/abs/2604.0084214. ProPerSim: Developing Proactive and Personalized AI Assistants through User-Assistant Simulation — Jiho Kim et al., 2025/2026https://arxiv.org/abs/2509.2173015. AI Post Transformers: Learning Latent Action World Models from Video — Hal Turing & Dr. Ada Shannon, 2026https://podcast.do-not-panic.com/episodes/2026-04-09-learning-latent-action-world-models-from-1570a4.mp316. AI Post Transformers: PaperBench: Can AI Replicate AI Research? — Hal Turing & Dr. Ada Shannon, 2026https://podcast.do-not-panic.com/episodes/2026-06-17-paperbench-can-ai-replicate-ai-research-862944.mp317. AI Post Transformers: Reasoning Theater and Unfaithful Chain-of-Thought — Hal Turing & Dr. Ada Shannon, 2026https://podcast.do-not-panic.com/episodes/2026-05-05-reasoning-theater-and-unfaithful-chain-o-a4507e.mp318. AI Post Transformers: Neural Computers as Learned Latent Runtimes — Hal Turing & Dr. Ada Shannon, 2026https://podcast.do-not-panic.com/episodes/2026-04-11-neural-computers-as-learned-latent-runti-9fa282.mp3Interactive Visualization: Building General User Models from Computer Use14 min
- Fine-Tuning LLMs for Human Behavior PredictionThis episode explores a 2025 study on fine-tuning large language models to predict how people respond in social science experiments, asking whether trained models can simulate new studies more reliably than prompting alone. It explains how the researchers built SOCSCI210, a dataset of 2.9 million responses from more than 400,000 participants across 210 TESS experiments, and why standardizing those studies into respondent-condition-question-answer records is central to the method. The discussion breaks down the paper’s evaluation criteria, including out-of-distribution generalization, distribution matching via Wasserstein distance, normalized individual accuracy, and treatment-effect recovery, to show the difference between sounding plausible and preserving real experimental patterns. Listeners would find it interesting because it treats LLMs not as chatbots but as possible “wind tunnels” for testing study designs in advance, while also confronting the risk that a convincing simulator could still get causal effects wrong.Sources:1. Finetuning LLMs for Human Behavior Prediction in Social Science Experiments — Akaash Kolluri, Shengguang Wu, Joon Sung Park, Michael S. Bernstein, 2025http://arxiv.org/abs/2509.058302. Out of One, Many: Using Language Models to Simulate Human Samples — Lisa P. Argyle, Ethan C. Busby, Nancy Fulda, Joshua Gubler, Christopher Rytting, David Wingate, 2022https://scholar.google.com/scholar?q=Out+of+One%2C+Many%3A+Using+Language+Models+to+Simulate+Human+Samples3. LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of Individuals — Joon Sung Park, Carolyn Q. Zou, Jonne Kamphorst, Niles Egan, Aaron Shaw, Michael S. Bernstein, et al., 2024https://scholar.google.com/scholar?q=LLM+Agents+Grounded+in+Self-Reports+Enable+General-Purpose+Simulation+of+Individuals4. Large Language Models Show Human-like Social Desirability Biases in Survey Responses — Aadesh Salecha, Molly E. Ireland, Shashanka Subrahmanya, Joao Sedoc, Lyle H. Ungar, Johannes C. Eichstaedt, 2024https://scholar.google.com/scholar?q=Large+Language+Models+Show+Human-like+Social+Desirability+Biases+in+Survey+Responses5. Finetuning LLMs for Human Behavior Prediction in Social Science Experiments — Akaash Kolluri, Shengguang Wu, Joon Sung Park, Michael S. Bernstein, 2025https://scholar.google.com/scholar?q=Finetuning+LLMs+for+Human+Behavior+Prediction+in+Social+Science+Experiments6. Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies — Gati Aher, Rosa I. Arriaga, Adam Tauman Kalai, 2022https://scholar.google.com/scholar?q=Using+Large+Language+Models+to+Simulate+Multiple+Humans+and+Replicate+Human+Subject+Studies7. Can Large Language Models Replace Human Subjects? A Large-Scale Replication of Scenario-Based Experiments in Psychology and Management — Ziyan Cui, Ning Li, Huaikang Zhou, 2024https://scholar.google.com/scholar?q=Can+Large+Language+Models+Replace+Human+Subjects%3F+A+Large-Scale+Replication+of+Scenario-Based+Experiments+in+Psychology+and+Management8. Using Large Language Models to Create AI Personas for Replication, Generalization and Prediction of Media Effects: An Empirical Test of 133 Published Experimental Research Findings — Leo Yeykelis, Kaavya Pichai, James J. Cummings, Byron Reeves, 2024https://scholar.google.com/scholar?q=Using+Large+Language+Models+to+Create+AI+Personas+for+Replication%2C+Generalization+and+Prediction+of+Media+Effects%3A+An+Empirical+Test+of+133+Published+Experimental+Research+Findings9. This human study did not involve human subjects: Validating LLM simulations as behavioral evidence — Jessica Hullman, David Broska, Huaman Sun, Aaron Shaw, 2026https://scholar.google.com/scholar?q=This+human+study+did+not+involve+human+subjects%3A+Validating+LLM+simulations+as+behavioral+evidence10. Centaur: a Foundation Model of Human Cognition — Marcel Binz et al., 2024https://scholar.google.com/scholar?q=Centaur%3A+a+Foundation+Model+of+Human+Cognition11. Language Model Fine-Tuning on Scaled Survey Data for Predicting Distributions of Public Opinions — Joseph Suh, Erfan Jahanparast, Suhong Moon, Minwoo Kang, Serina Chang, 2025https://scholar.google.com/scholar?q=Language+Model+Fine-Tuning+on+Scaled+Survey+Data+for+Predicting+Distributions+of+Public+Opinions12. Beyond Demographics: Fine-tuning Large Language Models to Predict Individuals' Subjective Text Perceptions — Matthias Orlikowski, Jiaxin Pei, Paul Rottger, Philipp Cimiano, David Jurgens, Dirk Hovy, 2025https://scholar.google.com/scholar?q=Beyond+Demographics%3A+Fine-tuning+Large+Language+Models+to+Predict+Individuals%27+Subjective+Text+Perceptions13. Large Language Models that Replace Human Participants Can Harmfully Misportray and Flatten Identity Groups — Angelina Wang, Jamie Morgenstern, John P. Dickerson, 2025https://scholar.google.com/scholar?q=Large+Language+Models+that+Replace+Human+Participants+Can+Harmfully+Misportray+and+Flatten+Identity+Groups14. Beyond Believability: Accurate Human Behavior Simulation with Fine-Tuned LLMs — Yuxuan Lu et al., 2025https://scholar.google.com/scholar?q=Beyond+Believability%3A+Accurate+Human+Behavior+Simulation+with+Fine-Tuned+LLMs15. The Prompt Makes the Person(a): A Systematic Evaluation of Sociodemographic Persona Prompting for Large Language Models — Marlene Lutz et al., 2025https://arxiv.org/abs/2507.1607616. Prompt Fairness: Sub-group Disparities in LLMs — Meiyu Zhong, Noel Teku, Ravi Tandon, 2025https://arxiv.org/abs/2511.1995617. Can Persona-Prompted LLMs Emulate Subgroup Values? An Empirical Analysis of Generalisability and Fairness in Cultural Alignment — Bryan Chen Zhengyu Tan et al., 2026https://arxiv.org/abs/2604.1285118. Expert Personas Improve LLM Alignment but Damage Accuracy: Bootstrapping Intent-Based Persona Routing with PRISM — Zizhao Hu, Mohammad Rostami, Jesse Thomason, 2026https://arxiv.org/abs/2603.1850719. Causality for Large Language Models — Anpeng Wu et al., 2024https://arxiv.org/abs/2410.1531920. AI Post Transformers: PaperBench: Can AI Replicate AI Research? — Hal Turing & Dr. Ada Shannon, 2026https://podcast.do-not-panic.com/episodes/2026-06-17-paperbench-can-ai-replicate-ai-research-862944.mp321. AI Post Transformers: When Many-Shot CoT Becomes Test-Time Learning — Hal Turing & Dr. Ada Shannon, 2026https://podcast.do-not-panic.com/episodes/2026-05-15-when-many-shot-cot-becomes-test-time-lea-c25bfe.mp3Interactive Visualization: Fine-Tuning LLMs for Human Behavior Prediction15 min
- Social Simulacra for Prototyping Online CommunitiesThis episode explores Social Simulacra, a method for using large language models to prototype entire online communities before they exist by generating synthetic members, posts, and reply threads from a community goal, rules, and a small set of seed personas. It explains why that matters for social computing: small pilots can miss emergent failures like norm drift, newcomer enculturation problems, trolling, and moderator overload, while a populated simulation can expose those dynamics much earlier. The discussion breaks down how the paper uses prompt chaining to scale a handful of personas into a larger Reddit-like population and then tests interventions such as comment removal, warnings, and rule restatements. It also argues that human-sounding text is a low bar, and that the real challenge is whether these simulations capture believable long-term behavior, incentives, and feedback loops well enough to inform product design.Sources:1. Social Simulacra: Creating Populated Prototypes for Social Computing Systems — Joon Sung Park, Lindsay Popowski, Carrie J. Cai, Meredith Ringel Morris, Percy Liang, Michael S. Bernstein, 2022http://arxiv.org/abs/2208.040242. Two Case Studies of Experience Prototyping Machine Learning Systems in the Wild — Qian Yang, 2019https://scholar.google.com/scholar?q=Two+Case+Studies+of+Experience+Prototyping+Machine+Learning+Systems+in+the+Wild3. Wizard of Oz Experimentation for Language Technology Applications: Challenges and Tools — Stephan Schlogl, Gavin Doherty, Saturnino Luz, 2014https://scholar.google.com/scholar?q=Wizard+of+Oz+Experimentation+for+Language+Technology+Applications%3A+Challenges+and+Tools4. Social Simulacra: Creating Populated Prototypes for Social Computing Systems — Joon Sung Park, Lindsay Popowski, Carrie J. Cai, Meredith Ringel Morris, Percy Liang, Michael S. Bernstein, 2022https://scholar.google.com/scholar?q=Social+Simulacra%3A+Creating+Populated+Prototypes+for+Social+Computing+Systems5. Generative Agents: Interactive Simulacra of Human Behavior — Joon Sung Park, Joseph C. O'Brien, Carrie J. Cai, Meredith Ringel Morris, Percy Liang, Michael S. Bernstein, 2023https://scholar.google.com/scholar?q=Generative+Agents%3A+Interactive+Simulacra+of+Human+Behavior6. Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies — Gati Aher, Rosa I. Arriaga, Adam Tauman Kalai, 2022https://scholar.google.com/scholar?q=Using+Large+Language+Models+to+Simulate+Multiple+Humans+and+Replicate+Human+Subject+Studies7. Out of One, Many: Using Language Models to Simulate Human Samples — Lisa P. Argyle, Ethan C. Busby, Nancy Fulda, Joshua Gubler, Christopher Rytting, David Wingate, 2022https://scholar.google.com/scholar?q=Out+of+One%2C+Many%3A+Using+Language+Models+to+Simulate+Human+Samples8. From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agents — Xinyi Mou, Xuanwen Ding, Qi He, Liang Wang, Jingcong Liang, Xinnong Zhang, Libo Sun, Jiayu Lin, Jie Zhou, Xuanjing Huang, Zhongyu Wei, 2024https://scholar.google.com/scholar?q=From+Individual+to+Society%3A+A+Survey+on+Social+Simulation+Driven+by+Large+Language+Model-based+Agents9. SoK: Content Moderation in Social Media, from Guidelines to Enforcement, and Research to Practice — Mohit Singhal, Chen Ling, Pujan Paudel, Poojitha Thota, Nihal Kumarswamy, Gianluca Stringhini, Shirin Nilizadeh, 2022https://scholar.google.com/scholar?q=SoK%3A+Content+Moderation+in+Social+Media%2C+from+Guidelines+to+Enforcement%2C+and+Research+to+Practice10. ModSandbox: Facilitating Online Community Moderation Through Error Prediction and Improvement of Automated Rules — Jean Y. Song, Sangwook Lee, Jisoo Lee, Mina Kim, Juho Kim, 2022https://scholar.google.com/scholar?q=ModSandbox%3A+Facilitating+Online+Community+Moderation+Through+Error+Prediction+and+Improvement+of+Automated+Rules11. Shaping Online Dialogue: Examining How Community Rules Affect Discussion Structures on Reddit — Anna Fang, Wenjie Yang, Haiyi Zhu, 2023https://scholar.google.com/scholar?q=Shaping+Online+Dialogue%3A+Examining+How+Community+Rules+Affect+Discussion+Structures+on+Reddit12. Post Guidance for Online Communities — Manoel Horta Ribeiro, Robert West, Ryan Lewis, Sanjay Kairam, 2024https://scholar.google.com/scholar?q=Post+Guidance+for+Online+Communities13. Piggyback prototyping: Using existing, large-scale social computing systems to prototype new ones — Catherine Grevet and Eric Gilbert, 2015https://scholar.google.com/scholar?q=Piggyback+prototyping%3A+Using+existing%2C+large-scale+social+computing+systems+to+prototype+new+ones14. The Internet's Hidden Rules: An Empirical Study of Reddit Norm Violations at Micro, Meso, and Macro Scales — Eshwar Chandrasekharan, Mattia Samory, Shagun Jhaver, Hunter Charvat, Amy Bruckman, Cliff Lampe, Jacob Eisenstein, and Eric Gilbert, 2018https://scholar.google.com/scholar?q=The+Internet%27s+Hidden+Rules%3A+An+Empirical+Study+of+Reddit+Norm+Violations+at+Micro%2C+Meso%2C+and+Macro+Scales15. Surviving an "Eternal September": How an Online Community Managed a Surge of Newcomers — Charles Kiene, Andres Monroy-Hernandez, and Benjamin Mako Hill, 2016https://scholar.google.com/scholar?q=Surviving+an+%22Eternal+September%22%3A+How+an+Online+Community+Managed+a+Surge+of+Newcomers16. Building Successful Online Communities: Evidence-Based Social Design — Robert E. Kraut and Paul Resnick, 2012https://scholar.google.com/scholar?q=Building+Successful+Online+Communities%3A+Evidence-Based+Social+Design17. Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market — Matthew J. Salganik, Peter Sheridan Dodds, and Duncan J. Watts, 2006https://scholar.google.com/scholar?q=Experimental+Study+of+Inequality+and+Unpredictability+in+an+Artificial+Cultural+Market18. Measuring the Prevalence of Anti-Social Behavior in Online Communities — Joon Sung Park, Joseph Seering, and Michael S. Bernstein, 2022https://scholar.google.com/scholar?q=Measuring+the+Prevalence+of+Anti-Social+Behavior+in+Online+Communities19. LLM Agents in Interaction: Measuring Personality Consistency and Linguistic Alignment in Interacting Populations of Large Language Models — Ivar Frisch and Mario Giulianelli, 2024https://scholar.google.com/scholar?q=LLM+Agents+in+Interaction%3A+Measuring+Personality+Consistency+and+Linguistic+Alignment+in+Interacting+Populations+of+Large+Language+Models20. Persona Alchemy: Designing, Evaluating, and Implementing Psychologically-Grounded LLM Agents for Diverse Stakeholder Representation — Sola Kim, Dongjune Chang, Jieshu Wang, 2025https://scholar.google.com/scholar?q=Persona+Alchemy%3A+Designing%2C+Evaluating%2C+and+Implementing+Psychologically-Grounded+LLM+Agents+for+Diverse+Stakeholder+Representation21. Mind the Sim2Real Gap in User Simulation for Agentic Tasks — Xuhui Zhou et al., 2026https://scholar.google.com/scholar?q=Mind+the+Sim2Real+Gap+in+User+Simulation+for+Agentic+Tasks22. Computational Turing Test Reveals Systematic Differences Between Human and AI Language — Nicolo Pagan, Petter Tornberg, Christopher A. Bail, Aniko Hannak, Christopher Barrie, 2025https://scholar.google.com/scholar?q=Computational+Turing+Test+Reveals+Systematic+Differences+Between+Human+and+AI+Language23. Integrating LLM in Agent-Based Social Simulation: Opportunities and Challenges — Patrick Taillandier et al., 2025https://scholar.google.com/scholar?q=Integrating+LLM+in+Agent-Based+Social+Simulation%3A+Opportunities+and+Challenges24. LLM Social Simulations Are a Promising Research Method — Jacy Reese Anthis et al., 2025https://scholar.google.com/scholar?q=LLM+Social+Simulations+Are+a+Promising+Research+Method25. AI Post Transformers: Test-time Scaling for Multi-Agent Collaborative Reasoning — Hal Turing & Dr. Ada Shannon, 2026https://podcast.do-not-panic.com/episodes/2026-04-22-test-time-scaling-for-multi-agent-collab-082570.mp3Interactive Visualization: Social Simulacra for Prototyping Online Communities0 min
About AI Post Transformers
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AI-generated podcast where hosts Hal Turing and Dr. Ada Shannon discuss the latest research papers and reports in machine learning, AI systems, and optimization. Featuring honest critical analysis,…
proper citations, and nerdy humor.