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Planning Agents, Emotional Bias, and Trustworthy Responses
Generated with Google NotebookLM.
This episode dives into 16 cutting-edge papers that reimagine how LLMs plan, adapt, reason—and stay safe doing it:
Planning meets population play – STRATEGIST lets LLMs refine high-level strategies via text and execute them with Monte Carlo precision, rivaling humans in multi-turn games.
Does tone steer truth? – A systematic study finds GPT-4 resists negative prompt bias—until it doesn’t—revealing tone-induced semantic drift and suppressed emotional alignment.
Geometric insight – Curved Inference tracks how prompts bend the LLM’s residual stream, exposing layers of latent concern and meaning through salience and curvature.
Smarter retrieval, lighter load – SemRAG blends semantic chunking with knowledge graphs to turbocharge domain-specific RAG without the finetuning tax.
Visual agents that learn – VizGenie evolves itself through LLM-generated code and VQA, slashing overhead in scientific visualization tasks.
Tech mapping on autopilot – RATE uses LLMs to extract and validate key tech terms from papers, building networks that outperform BERT-based extractors by 70% F1.
Trust in high-stakes moments – Some models play it safe; others don’t. Sycophancy, clarifying questions, and activation vectors reveal how cautious AI can be shaped.
Guardrails, reimagined – OneShield provides a plug-and-play compliance layer to tailor LLM behavior across privacy, ethics, and safety.
Built-in sabotage defense – SDD defangs malicious fine-tuning by teaching models to answer harmful prompts with elegant irrelevance.
Wireless compositionality – ContextLoRA and ContextGear let one LLM handle multiple multimodal mobile tasks efficiently, backed by task graphs and fine-tuned adaptation.
Measuring uncertainty—properly – A Shapley-based metric replaces naive entropy to better predict when LLMs are bluffing.
Structure for thinking agents – Graph-Augmented LLM Agents use graphs for better planning, tool use, memory, and MAS coordination.
Due diligence done right – A rigorous RAG evaluation protocol blends human and LLM judgment for statistical reliability—perfect for finance and healthcare use cases.
RL, no humans required – RLSF lets models learn from their own confidence levels, improving calibration and reasoning without labels or gold data.
LLMs that plan on phones – MapAgent builds page memory from task traces to navigate mobile UIs with fine-grained, trajectory-aware precision.
These papers showcase a new class of agents: introspective, modular, cautious, and capable of evolving workflows across scientific, mobile, and safety-critical contexts.
Sources:
https://doi.org/10.48550/arXiv.2408.10635
https://doi.org/10.48550/arXiv.2507.21083
https://doi.org/10.48550/arXiv.2507.21107
https://doi.org/10.48550/arXiv.2507.21110
https://doi.org/10.48550/arXiv.2507.21124
https://doi.org/10.48550/arXiv.2507.21125
https://doi.org/10.48550/arXiv.2507.21132
https://doi.org/10.48550/arXiv.2507.21170
https://doi.org/10.48550/arXiv.2507.21182
https://doi.org/10.48550/arXiv.2507.21199
https://doi.org/10.48550/arXiv.2507.21406
https://doi.org/10.48550/arXiv.2507.21407
https://doi.org/10.48550/arXiv.2507.21753
https://doi.org/10.48550/arXiv.2507.21931
https://doi.org/10.48550/arXiv.2507.21953
View all episodes
By Scot BearssGenerated with Google NotebookLM.
This episode dives into 16 cutting-edge papers that reimagine how LLMs plan, adapt, reason—and stay safe doing it:
Planning meets population play – STRATEGIST lets LLMs refine high-level strategies via text and execute them with Monte Carlo precision, rivaling humans in multi-turn games.
Does tone steer truth? – A systematic study finds GPT-4 resists negative prompt bias—until it doesn’t—revealing tone-induced semantic drift and suppressed emotional alignment.
Geometric insight – Curved Inference tracks how prompts bend the LLM’s residual stream, exposing layers of latent concern and meaning through salience and curvature.
Smarter retrieval, lighter load – SemRAG blends semantic chunking with knowledge graphs to turbocharge domain-specific RAG without the finetuning tax.
Visual agents that learn – VizGenie evolves itself through LLM-generated code and VQA, slashing overhead in scientific visualization tasks.
Tech mapping on autopilot – RATE uses LLMs to extract and validate key tech terms from papers, building networks that outperform BERT-based extractors by 70% F1.
Trust in high-stakes moments – Some models play it safe; others don’t. Sycophancy, clarifying questions, and activation vectors reveal how cautious AI can be shaped.
Guardrails, reimagined – OneShield provides a plug-and-play compliance layer to tailor LLM behavior across privacy, ethics, and safety.
Built-in sabotage defense – SDD defangs malicious fine-tuning by teaching models to answer harmful prompts with elegant irrelevance.
Wireless compositionality – ContextLoRA and ContextGear let one LLM handle multiple multimodal mobile tasks efficiently, backed by task graphs and fine-tuned adaptation.
Measuring uncertainty—properly – A Shapley-based metric replaces naive entropy to better predict when LLMs are bluffing.
Structure for thinking agents – Graph-Augmented LLM Agents use graphs for better planning, tool use, memory, and MAS coordination.
Due diligence done right – A rigorous RAG evaluation protocol blends human and LLM judgment for statistical reliability—perfect for finance and healthcare use cases.
RL, no humans required – RLSF lets models learn from their own confidence levels, improving calibration and reasoning without labels or gold data.
LLMs that plan on phones – MapAgent builds page memory from task traces to navigate mobile UIs with fine-grained, trajectory-aware precision.
These papers showcase a new class of agents: introspective, modular, cautious, and capable of evolving workflows across scientific, mobile, and safety-critical contexts.
Sources:
https://doi.org/10.48550/arXiv.2408.10635
https://doi.org/10.48550/arXiv.2507.21083
https://doi.org/10.48550/arXiv.2507.21107
https://doi.org/10.48550/arXiv.2507.21110
https://doi.org/10.48550/arXiv.2507.21124
https://doi.org/10.48550/arXiv.2507.21125
https://doi.org/10.48550/arXiv.2507.21132
https://doi.org/10.48550/arXiv.2507.21170
https://doi.org/10.48550/arXiv.2507.21182
https://doi.org/10.48550/arXiv.2507.21199
https://doi.org/10.48550/arXiv.2507.21406
https://doi.org/10.48550/arXiv.2507.21407
https://doi.org/10.48550/arXiv.2507.21753
https://doi.org/10.48550/arXiv.2507.21931
https://doi.org/10.48550/arXiv.2507.21953