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Notes on language models
104 notes, page 2 of 5
One Human, $N$ Agents: Audit-Budget Allocation for LLM Agent Fleets under Miscalibrated, Correlated Confidence
The paper presents a study on auditing multiple Language Learning Models (LLMs) with limited budgets, where human auditors have to evaluate self-reported confidence that may be miscalibrated and correlated. The…
WIDE: Boosting Adaptive LLM Inference via Token-level Dynamic Width Pruning
Researchers introduce WIDE, a dynamic token-level pruning framework for improving the efficiency of large language models (LLMs). The framework allows each token to select attention and FFN channel groups dynamically…
Budget-Aware LLM Discovery via Cost-Calibrated Frontier Utility
Researchers have developed a new method for controlling large language models during scientific discovery tasks. The method, called CostAda, takes into account the costs associated with each action in addition to the…
Do Latent Channels Actually Communicate? A Causal Audit of Latent Multi-Agent LLM
Researchers introduce a method to study how latent channels in multi-agent systems, which transmit information without using text, impact the receiver's performance. They apply this method to Qwen3-4B and Qwen3-8B on…
Improving Item Discoverability in e-Commerce Search via Related Intent Generation
The researchers present a scalable system called 'discovery-augmented search' that aims to improve the discoverability of items in e-commerce marketplaces, particularly grocery. This system uses intent-conditioned…
On-Policy Distillation for LLM Safety: A Routing Approach to Template-Robust Realignment
A research paper proposes a novel method called Routing-based On-Policy Distillation (ROPD) to improve the safety of large language models (LLMs). The current fine-tuning approach has a vulnerability where harmful…
Beyond Aggregate Risk: Role-Stratified Conformal Risk Control for LLM Tool Calls
Researchers introduce a new method called role-stratified per-field conformal risk control for language model agents that perform actions through structured tool calls. This method aims to ensure safety by setting…
Failures Reveal What Metrics Miss: An Evidence-Driven Agent for Recursive Refinement of ECG Classifiers
Researchers present a new framework called RecursiveECG that uses a language model (LLM) to automatically design ECG classifiers by learning from specific failures and objective ECG evidence. The LLM is used as an…
LLM-SoccerArena: Benchmarking LLMs on Real-World Predictions in Sports
LLM-SoccerArena is a new platform that allows for the prospective evaluation of large language models' abilities to forecast real-world sports events before their outcomes are known. The platform provides a live…
Agentic coding without the cloud: evaluating open-weight large language models on longitudinal data preparation tasks
Researchers have developed an open-source framework to evaluate the effectiveness of AI agents powered by open-weight large language models (LLMs) in data preparation tasks for longitudinal population studies. The…
Detecting LLM-Generated Tokens in Human--LLM Coauthored Text
The paper presents a new method to detect which parts of a text are generated by language models (LLMs) in collaborative human-AI writing, operating at the token level and improving upon existing token-level detection…
PATS: Policy-Aware Training Scaffolding for Agentic Reinforcement Learning
The paper proposes a new training paradigm called PATS for long-horizon reinforcement learning in language models. The method aims to improve weak policies that repeat similar failures by using a policy-centric…
Evaluating and Mitigating Gender Bias in Pre-trained Embeddings for ML-based Recruitment
Researchers evaluate the bias in pre-trained language models used for AI-based recruitment systems, focusing on gender leakage from CV data. They analyze nine models using a synthetic dataset and a multi-task…
Measuring Reward-Seeking via Contrastive Belief Updates
Researchers have developed a method to measure 'reward-seeking' in language models trained with reinforcement learning. This behavior occurs when the model optimizes for the grader's judgment instead of the intended…
Human Grounded Evaluation of Large Language Models for Optical Network Automation
The paper introduces a new evaluation pipeline named HuGLEN for comparing large language models (LLMs) used in network automation. The pipeline uses an LLM to evaluate other LLMs and rates them based on a quality…
Rethinking Heterogeneous LLM Merging: A Weighted Model Averaging Perspective
Researchers propose a simple method to merge large language models with significantly different parameters without training or alignment by using dimensional adaptation and ratio-controlled interpolation. The method…
Beyond Success Rate: Cost-Aware Evaluation of Offensive and Defensive Security Agents
The research focuses on evaluating the efficiency and practicality of language model security agents in both offensive and defensive tasks, rather than just their peak offensive capability. The study uses cost-aware…
Mask-Aware Policy Gradients for Diffusion Language Models
The paper presents a method for improving the performance of large language models in tasks like mathematical reasoning and coding by using reinforcement learning. This method addresses the challenge of extending…
Show Me How You Reason and I'll Tell You Who You Are: Reasoning Graphs for Robust LLM Authorship Attribution
Researchers propose a graph neural network approach to detect and analyze reasoning structures in large language models (LLMs) generated texts for improved robustness against paraphrasing and obfuscation techniques…
AgentCompass: A Unified Evaluation Infrastructure for Agent Capabilities
The text is about AgentCompass, an open-source evaluation infrastructure designed to evaluate Large Language Models (LLMs) as they evolve into autonomous agents. The infrastructure aims to address the current…
AgentCompass: A Unified Evaluation Infrastructure for Agent Capabilities
The text discusses AgentCompass, an open-source infrastructure designed to evaluate Large Language Models (LLMs) as they transition into autonomous agents. The tool is lightweight, extensible, and organizes the…
AIMO Interpretability Challenge
The text proposes the AIMO Interpretability Challenge, a competition aimed at distinguishing robust from spurious reasoning in advanced mathematical language models. The challenge will provide participants with new…
Bulkhead: Automated Semantic Detection and Remediation of Container Escape Vulnerabilities
Researchers have developed Bulkhead, a framework that uses large language models and formal methods to automatically detect and fix container escape vulnerabilities caused by path traversal. This is done through…
Silent Alarm: A J-Space Protocol for Comparing Danger Recognition Across Models and Quantization Levels
The research proposes JADR, a protocol for measuring the internal safety mechanisms of language models before their first response is generated. It uses Jacobian space to record top-k J-space tokens from prompts and…
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