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Notes on large language models
160 notes, page 2 of 7
Agentic Reinforcement Learning with Observation-Calibrated Self-Distillation
Researchers propose a new method called Observation-Calibrated Self-Distillation (OCSD) to train large language models in reinforcement learning. OCSD addresses a confounding issue in previous methods by comparing two…
From Score Matrices to Football-Aware Match-State Simulation: An Auditable LLM Harness for Exact-Score Reranking
The paper presents an auditable information harness that combines dynamic Poisson-family models and large language models (LLMs) to forecast football scores. The system goes through four iterations, each improving on…
The Effect of Perceived Race and Gender on Police Language Use: Experimental Evidence from VR Simulations
Researchers studied how police officers speak to virtual characters depicted as Black adult males in VR simulations. The study found that most officers speak less deferentially to Black male characters, except for…
MemHarness: Memory Is Reconstructed, Not Replayed
Researchers have developed MemHarness, a framework to improve large language model agents by enabling them to adapt and reconstruct past experiences based on the current context, instead of simply replaying stored…
PAIChecker: Uncovering and Checking PR-Issue Misalignment in SWE-Bench-Like Benchmarks
Researchers present PAIChecker, a system designed to detect misalignment between Pull Requests (PRs) and Issues in SWE-bench-like benchmarks used for evaluating Large Language Models (LLMs). The system identifies…
PAIChecker: Uncovering and Checking PR-Issue Misalignment in SWE-Bench-Like Benchmarks
The paper proposes a system called PAIChecker to check for misalignment between Pull Requests (PR) and Issues in SWE-bench-like benchmarks used for evaluating Large Language Models' issue resolution capability. The…
When Derived Measurements Mislead: Quantifying and Mitigating LLM Over-Trust with Privileged-Modality Reliability Evidence
The study presents a framework to quantify and address Derived-Feature Over-Trust (DFOT), which occurs when large language models (LLMs) misuse derived measurements as if they were direct facts. The researchers use…
When Specifications Conflict: A Symmetry-Based Framework for Measuring LLM Preferences
The researchers propose a framework to analyze how large language models (LLMs) handle conflicting specifications when integrating multiple sources of information. They compare four types of representation: pure…
Do Latent Channels Actually Communicate? A Causal Audit of Latent Multi-Agent LLM
Researchers have developed a method to evaluate if latent channels in large language models effectively communicate task-relevant information in multi-agent systems. The study applies this method to Qwen3 and Qwen3-8B…
Linguistic Monoculture in LLM-Assisted Language Use
The study discusses the potential effects of relying on large language models (LLMs) for writing and communication. It suggests that widespread use could lead to a decrease in linguistic variety, a phenomenon referred…
Scores Are Not Decisions: Cost-Aware Stopping for Tool Acquisition in LLM Agents
A new approach, Cost-Aware Marginal Decision-Focused Stopping (CAM-DF), is proposed for tool selection in Large Language Model (LLM) agents that use external services. The method helps address the challenge of…
Agentic Permissions Policy Algebra for Taint Confinement in LLM Agents
Researchers have developed APPA (Agentic Permissions Policy Algebra), an Information Flow Control (IFC) framework designed to enhance security in Large Language Model (LLM) agents that handle mixed-confidentiality data…
Closed-Loop Validation-Repair for Healthcare Interoperability: A Multi-Model Study of Schema Compliance in Clinical LLMs
A study evaluates three large language models (Qwen2.5 7B, Llama 3.1 8B, and Gemma2 9B) on their compliance with healthcare standards such as ICD-10, CPT, and HL7 FHIR in 320 clinical scenarios. The study found that…
DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data
Researchers propose a framework called DataOrchestra that adapts data processing strategies for each example in Large Language Models (LLMs) pretraining, improving performance across 11 benchmarks.
DeepFaith: Evidence-Grounded LLMs for Faithful Incident Reporting in Multi-Stage APT Defense
In this paper, the authors propose a framework called DeepFaith to create more accurate and reliable incident reports in multi-stage APT defense. The framework uses large language models (LLMs) and ensures that…
Efficient LLM-Generated Shuttling Compilers for Complex Trapped-Ion Architectures
Researchers present a study where they use a large language model (LLM) called Claude Opus 4.7 to generate and refine Python code for shuttling compilers in trapped-ion quantum computers. The compilers are designed for…
Efficient LLM-Generated Shuttling Compilers for Complex Trapped-Ion Architectures
Researchers used a large language model named Claude Opus 4.7 to generate and refine Python code for shuttling compilers in trapped-ion quantum computers. The study covers three types of architectures: linear segmented…
From Proprietary to Open-Source: Bridging the Distribution Gap via Multi-Agent Protocol Distillation in Agentic Search
Researchers have proposed a new method called Multi-Agent Protocol Distillation (MAPD) to improve the performance of large language models in solving knowledge-intensive tasks. This method addresses the challenge of…
Gubernaut: A Deterministic Homeostatic Controller for Affect-Regulated LLM Agents, Validated Across Independent Model Families
The research introduces Gubernaut Cognitive Controller (GCC), a model-agnostic runtime control layer designed to regulate the behavior of large language models (LLMs) that can exhibit reactive failure modes such as…
LEX-EC: A Lexical Evidence-Channel Audit Framework for Zero-Shot LLM Personality Classification in Black-Box Settings
Researchers introduce LEX-EC, a framework for analyzing the interpretability of large language models in assigning personality labels from text. The framework identifies and separates marginal distribution effects from…
Reason-Mediated Behavioral Models for Auditing LLM Social Simulators
Researchers propose a method to evaluate large language models used as social simulators, such as synthetic survey respondents. They argue that simply checking if the simulated outcomes match human outcomes is not…
The Tokenizer Tax: Quantifying and Explaining the Cross-Lingual Cost of Subword Tokenization for Indian Languages
The research quantifies the disadvantage faced by many non-English languages in large language models (LLMs) due to subword tokenizers that are primarily trained on English corpora. The study focuses on Indian…
Agentic coding without the cloud: evaluating open-weight large language models on longitudinal data preparation tasks
Researchers have developed an open-source framework for evaluating the effectiveness of AI agents powered by open-weight large language models in data preparation tasks, specifically focusing on longitudinal population…
Agentic coding without the cloud: evaluating open-weight large language models on longitudinal data preparation tasks
Researchers introduce an open-source framework for evaluating the effectiveness of AI agents powered by open-weight large language models (LLMs) in data preparation tasks, specifically for longitudinal population…
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