This research investigates how using artificial intelligence (AI) or machine learning (ML) to generate variables for economic regressions can lead to biased estimates and invalid statistical inference. While researchers often treat AI-generated outputs as standard data, the authors demonstrate that measurement error in these variables—even from high-performance algorithms—shifts the centering of confidence intervals, making them unreliable. To address these distortions, the paper introduces two practical solutions: a mathematical bias correction that does not require ground-truth validation data and a joint estimation framework that models the latent variables and regression parameters simultaneously. The effectiveness of these methods is illustrated through diverse applications, including job posting classifications, CEO time-use analysis, and central bank sentiment indexing. Ultimately, the study provides a robust toolkit for economists to maintain statistical integrity when integrating modern computational tools into empirical research.

This paper introduces Attention Matching (AM), a novel framework for fast and efficient key-value (KV) cache compaction in long-context language models. As models process longer sequences, the memory required for the KV cache becomes a major bottleneck, often necessitating lossy strategies like summarization or token eviction. The researchers propose optimizing compact keys and values to reproduce the original model's attention outputs and attention mass across every layer. This method achieves up to 50× compaction in seconds, significantly outperforming traditional token-dropping baselines and matching the quality of expensive gradient-based optimization. By incorporating nonuniform head budgets and scalar attention biases, AM maintains high downstream accuracy on complex reasoning tasks while remaining compatible with existing inference engines. Their findings suggest that latent-space compaction is a powerful primitive for managing the memory demands of modern generative AI.

This position paper argues against the anthropomorphization of intermediate tokens in large language models, commonly referred to as "reasoning traces" or "chains of thought." The authors contend that these outputs are not genuine reflections of human-like thinking but are instead statistically generated patterns that may lack semantic validity. Research indicates that model performance can improve even when these traces are factually incorrect or nonsensical, suggesting that the connection between a trace and the final answer is often tenuous. Consequently, viewing these tokens as an interpretable window into a model’s logic can lead to a dangerous overestimation of its reliability. The authors call on the scientific community to move away from human-centric metaphors and focus on external verification of solutions. By treating intermediate tokens as a computational tool for the model rather than an explanation for the user, researchers can pursue more effective and honest AI development.

Researchers at Google DeepMind introduced Code World Models (CWM), a framework that uses Large Language Models to translate natural language game rules and player trajectories into executable Python code. Unlike traditional methods that use LLMs as direct move-generating policies, this approach treats the model as a verifiable simulation engine capable of defining state transitions and legal actions. The generated code serves as a foundation for high-performance planning algorithms like Monte Carlo tree search (MCTS), which provides significantly greater strategic depth. The framework also synthesizes inference functions to estimate hidden states in imperfect information games and heuristic value functions to optimize search efficiency. Evaluated across ten diverse games, the CWM agent consistently matched or outperformed Gemini 2.5 Pro, demonstrating superior generalization on novel, out-of-distribution games. This shift from "intuitive" play to System 2 deliberation allows the agent to maintain formal rule adherence while scaling performance with increased computational power.

This research paper explores how Chain of Thought (CoT) prompting enables transformers to solve complex mathematical problems by mimicking iterative optimization techniques. The authors demonstrate that while standard models are limited to a single stage of calculation, using intermediate reasoning steps allows a transformer to execute multi-step gradient descent internally. Through the lens of linear regression tasks, the study proves that this autoregressive process leads to a near-perfect recovery of underlying data patterns that simpler models cannot capture. Furthermore, the findings indicate that looped architectures and CoT significantly boost the ability of these models to generalize to new information. Ultimately, the work provides a formal theoretical framework to explain why breaking down problems into smaller parts enhances the algorithmic power of large language models.

This paper explores how task descriptors, such as a mean value $\mu$, improve in-context learning (ICL) for linear regression within Transformer models. By examining a one-layer linear self-attention (LSA) network, the researchers demonstrate that models can effectively utilize these descriptors to standardize input data and reduce prediction errors. The paper provides a mathematical proof that gradient flow training converges to a global minimum, allowing the Transformer to simulate an optimized version of gradient descent. Through various experiments, the authors confirm that adding task information leads to superior performance compared to models without such context. Furthermore, the study reveals that while large sample sizes simplify the model's strategy, finite sample settings require the Transformer to develop more complex internal representations to manage bias and variance. These findings provide a theoretical foundation for the empirical success of prompts and instructions in large language models.

This research explores how modern Large Language Models adapt to new information during inference by framing in-context learning as a series of implicit weight updates. The authors demonstrate that the influence of a prompt can be mathematically mapped to specific, rank-1 patches on a model's existing parameters, effectively "reprogramming" the network without formal retraining. By establishing a framework of input and output controllability, the study proves this phenomenon applies to complex architectures like Gemma, Llama, and Mixture of Experts. Their experiments on Gemma 3 validate that a model with modified weights and no context produces the same outputs as the original model with a prompt. This work provides a mechanistic foundation for understanding how static pre-trained transformers dynamiclly transmute contextual cues into effective internal parameters.

This research explores the mechanisms of in-context learning (ICL) in Large Language Models, proposing that transformers learn by implicitly updating their internal weights during inference. The authors demonstrate that a transformer block effectively transforms prompt examples into a rank-1 weight update of the model's MLP layer. This process allows the model to adapt to new patterns without permanent training, mathematically mirroring stochastic gradient descent as tokens are processed. Theoretical formulas are provided to map these context-driven adjustments exactly, showing that MLP layers are naturally structured to absorb and store contextual information. Experimental results on linear regression tasks confirm that modifying model weights using these formulas produces identical predictions to providing the original in-context prompt. The study ultimately unifies ICL with model editing and steering vectors, offering a principled framework for understanding how LLMs reorganize their internal representations dynamically.

This research establishes a complete algorithmic framework for identifying counterfactual quantities by utilizing a newly discovered family of physically realizable Layer 3 data. While traditional causal inference was restricted to observational and interventional data, the authors introduce the CTFIDU+ algorithm, which can determine if a counterfactual query is identifiable from arbitrary sets of counterfactual distributions. The study defines a fundamental limit to exact causal inference, proving a duality where a query is only point-identifiable if it is also physically realizable through counterfactual randomization. For queries that remain non-identifiable, the authors derive novel analytic bounds that are significantly tighter than previous methods. These theoretical advancements are validated through simulations in fairness and personalized decision-making, demonstrating that access to counterfactual data yields more precise results in practice.

This paper investigates whether cosine similarity accurately reflects the semantic similarity of learned embeddings, particularly in linear matrix factorization models. The authors demonstrate that the metric can produce arbitrary or non-unique results because certain training objectives allow for the random rescaling of latent dimensions. While some regularization methods yield a unique solution, others leave the final similarity scores dependent on opaque modeling choices rather than the underlying data. These findings suggest that the common practice of applying cosine similarity to high-dimensional vectors may lead to misleading conclusions. Consequently, the researchers advise against the untested use of this metric and suggest alternative normalization or projection techniques to ensure more reliable measurements.