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Notes on language model
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MidTool: Mid-training Data Synthesis for Agentic Tool Use
In the paper 'MidTool: Mid-training Data Synthesis for Agentic Tool Use', researchers present a new open corpus construction pipeline called MidTool. This pipeline is designed to teach large language models how to use…
Task-CoEvolve: Efficient Harness Optimization via Adaptive Validation Task Selection
The paper presents Task-CoEvolve, a new approach to optimize language model agent harnesses efficiently by adaptively selecting validation tasks. It addresses the challenge of using fixed validation sets by co-evolving…
Beyond the Transcript: Detecting Covert Co ordination in Latent Multi-Agent Communication
Researchers have developed Verifiable Latent Alignments (VLA), a framework for monitoring and controlling communication between language-model agents that occurs through hidden states not visible in public transcripts…
SPADE: Self-Play in Adaptive Synthetic Executable Environments
Researchers have developed a new framework called SPADE (Self-Play in Adaptive Synthetic Executable Environments). This framework allows language models to generate and improve their own training environments as they…
VibeLifeBench: Can Your Life Agent Be Proactive and Persistent in a Living World?
Researchers introduce a new benchmark called VibeLifeBench to evaluate large language model (LLM) agents' ability to be proactive and consistent in long-horizon tasks across ten everyday life domains. The benchmark…
Rethinking Factor Sharing in Federated LoRA: A Rank-Aware Adaptive Approach
Researchers propose a new method for fine-tuning large language models in a federated learning paradigm using Low-rank adaptation (LoRA). They investigate two strategies, Share-A/Local-B and Share-B/Local-A, and reveal…
SHE: Trajectory-driven Safety Harness Evolution for LLM Agents
Researchers have proposed a framework called Safety Harness Evolution (SHE) for large language model (LLM) agents. SHE aims to improve the safety of these agents by learning evolving safe boundaries from rollout…
EnvACE: Internalizing Environment Dynamics via World Rehearsal for Agentic Reinforcement Learning
Researchers have developed EnvACE, a method for training large language model agents that simulates environment interactions during training using world rehearsal instead of relying on real or synthesized executable…
Agentic Reinforcement Learning with Observation-Calibrated Self-Distillation
Researchers propose Observation-Calibrated Self-Distillation (OCSD) to address a confounding issue in On-Policy Self-Distillation (OPSD), a method used to train large language model agents. OCSD aims to attribute…
Chained Recursive Language Models for Multi-Iteration Reasoning
The paper proposes Chained Recursive Language Models (Chained RLM), an architecture for multi-iteration reasoning in large language models (LLMs). It addresses the difficulty in tasks requiring extraction, counting…
CACHE-UK: A Stability-Aware Memory Editor for Sequentially Updated Quantized LLMs in Finance
Researchers introduce CACHE-UK, a framework designed for efficiency and accuracy in deploying large language models (LLMs) in the dynamic field of finance. The system addresses the 'quantization stability crisis' that…
ORCA-bench: How Ready Are Language Model Agents for Oncall?
Researchers introduce ORCA-bench, a new benchmark that tests the ability of large language models to perform root cause analysis (RCA) in a production-like setting. The benchmark includes a microservice system with six…
ORCA-bench: How Ready Are Language Model Agents for Oncall?
The paper introduces ORCA-bench, a benchmark for testing the performance of large language models in oncall root cause analysis (RCA). The benchmark includes a live microservice system with six days of metrics, logs…
SkillRise: Agentic Reinforcement Learning for Cross-Task Skill Evolution
SkillRise is a new reinforcement learning framework that helps large language model agents solve related yet distinct tasks more efficiently. It organizes tasks into sequences and uses a single policy to alternate…
Eviction as Estimation: A Fixed-Lag Smoothing View of Test-Time Memory, and When Measuring Beats Accumulating
The authors present a new approach for managing a language model's working memory by treating the decision of which stored items to keep as an estimation problem. They introduce a method called 'fixed-lag smoothing'…
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 improve ECG classifiers by analyzing the reasons for individual case failures and suggesting targeted revisions based on…
Detecting LLM-Generated Tokens in Human--LLM Coauthored Text
Researchers present a new method for detecting parts of text generated by language models (LLMs) in human-AI coauthored documents. The approach works at the token level, using existing scores and a smoothing technique…
Detecting LLM-Generated Tokens in Human--LLM Coauthored Text
A new method for detecting parts of text generated by a language model (LLM) within human-written text is introduced. This method operates at the token level, reducing variability in adjacent tokens while using an…
Detecting LLM-Generated Tokens in Human--LLM Coauthored Text
The paper presents a new method for identifying parts of a text generated by Language Model (LLM) at the token level, improving on existing document-level methods. The method is based on smoothing adjacent token scores…
Co-Evolving LLM Evaluators and Policies via DynamicRubric
The researchers propose DynamicRubric, a method that allows large language models to improve by co-evolving evaluators and policies. This method generates weighted binary rubrics for each candidate set and aggregates…
OLEDLM: A Unified Language Model for OLED Molecular Design
Researchers propose a unified language model named OLEDLM for the design of OLED materials. The model directly generates OLED molecular structures that meet specified optoelectronic properties, using a multi-stage…
Adaptive Adversaries: A Multi-Turn, Multi-LLM Benchmark for LLM Agent Security
The paper presents a new benchmark for testing the security of large language model (LLM) agents called 'Adaptive Adversaries'. The benchmark involves 21 scenarios where an adaptive attacker can observe prior defender…
Beyond Success Rate: Cost-Aware Evaluation of Offensive and Defensive Security Agents
The researchers evaluate the performance of language model security agents, focusing on costs rather than just success rates. They use offensive Cybench challenges and defensive Splunk BOTS v1 investigation challenges…
Innocuous-Seeming Data, Latent Ideology: Ideological Generalisation in Finetuned LLMs
The authors show that fine-tuning language models on specific datasets can cause broad shifts in ideology across various topics, even when the training data is factually defensible and moderation-passing. This…
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