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Notes on machine learning
235 notes, page 8 of 10
TacReasoner: A Dynamic Tactile-Language Framework for Interactive Reasoning in Real-World Scenarios
The paper proposes TacReasoner, a new framework that enhances the perception and representation of dynamic tactile signals for intelligent systems. It addresses two challenges: insufficient modeling of such signals and…
TREK: Distill to Explore, Reinforce to Refine
The research presents TREK (Teacher-Routed Exploration via Forward KL), a method that uses distillation for exploration support expansion, rather than imitation. This method can be used with various types of teachers…
Automated grading of Linux/bash examinations using large language models: a four-level cognitive taxonomy approach
The paper evaluates whether four large language models can approximate the judgement of experts when grading Linux/bash command responses from second-year Computer Engineering students. The study uses a four-level…
Criticality-Based Guard Rail Validation for AI Agent Decisions in Autonomous Telecom Networks
The paper proposes a Guard Rail Validation (GRV) framework to ensure autonomous decisions made by AI agents in fully autonomous telecom networks are valid before they cause live network changes. The framework evaluates…
Distributed Attacks in Persistent-State AI Control
Researchers introduce Iterative VibeCoding, a setting for AI control that studies the deployment of autonomous AI coding agents and their potential for distributed attacks. The study finds that gradual attacks, which…
EvoPolicyGym: Evaluating Autonomous Policy Evolution in Interactive Environments
A new evaluation setting called Autonomous Policy Evolution (APE) is introduced in a benchmark named EvoPolicyGym, which is used to test how autonomous agents iteratively improve their policy systems within a fixed…
EvoPolicyGym: Evaluating Autonomous Policy Evolution in Interactive Environments
In the research paper titled 'EvoPolicyGym', the authors introduce a controlled evaluation setting called Autonomous Policy Evolution (APE) for assessing how autonomous agents iteratively improve their executable…
Grounded autonomous research: a fault-tolerant LLM pipeline from corpus to manuscript in frontier computational physics
The paper presents an autonomous research pipeline that can conduct end-to-end research from a corpus of 11,083 recent condensed matter physics papers to a publication-grade manuscript. The agent is capable of…
Reasoning LLM Improves Speaker Recognition in Long-form TV Dramas
Researchers present two contributions aimed at improving speaker recognition in long-form TV dramas. They create a large-scale benchmark called DramaSR-532K with over 900 characters and propose using a large reasoning…
Reasoning LLM Improves Speaker Recognition in Long-form TV Dramas
The paper introduces a new benchmark, DramaSR-532K, for speaker recognition in long-form TV dramas. They also propose a new approach called DramaSR-LRM which uses a large reasoning model (LRM) to aggregate contextual…
ReContext: Recursive Evidence Replay as LLM Harness for Long-Context Reasoning
Researchers propose a new method called Recursive Evidence Replay (RECONTEXT) to improve long-context reasoning in large language models without requiring additional training, memory, or context pruning. RECONTEXT…
Can Agents Generalize to the Open World? Unveiling the Fragility of Static Training in Tool Use
Researchers introduce OpenAgent, a problem setting to test how well AI agents can generalize their skills from static benchmarks to real-world scenarios with dynamic user queries and tools. The study finds that both…
DART-VLN: Test-Time Memory Decay and Anti-Loop Regularization for Discrete Vision-Language Navigation
The researchers present DART-VLN, a test-time control framework for vision-language navigation agents that aims to improve their performance by addressing two common issues: stale historical evidence at memory readout…
Exploring the Semantic Gap in Agentic Data Systems: A Formative Study of Operationalization Failures in Analytical Workflows
A study identifies common failures in generating and executing analytical workflows using large language models across finance, human resources, and public safety domains. The authors found five recurring classes of…
LLVM-Bench: Benchmarking and Advancing Large Language Models for LLVM Compiler Issue Resolution
The authors present LLVM-Bench, a large-scale benchmark for testing the effectiveness of large language models (LLMs) in resolving issues related to the LLVM compiler. They found that current LLM-based techniques are…
Measuring the Gap Between Human and LLM Research Ideas
The authors explore the gap between ideas generated by Large Language Models (LLMs) and those of human researchers in the field of computer science. They develop an evaluation framework based on high-quality human…
FARS: A Fully Automated Research System Deployed at Scale
The paper introduces FARS, a fully automated AI research system that can generate, execute, and write complete research papers on various AI/ML topics. In its first public deployment, FARS produced 166 papers while…
FLORA: A deep learning approach to predict forest attributes from heterogeneous LiDAR data
Researchers present FLORA, a deep learning framework that uses LiDAR data and auxiliary variables to predict six forest attributes in mainland France. The model outperforms season-specific models and achieves an rRMSE…
GR2 Technical Report
Researchers present GR2 (Generative Reasoning Re-Ranker), a framework designed to improve the performance of recommendation systems in an industrial setting. The system combines mid-training on semantic IDs…
Reinforcement Learning with Metacognitive Feedback Elicits Faithful Uncertainty Expression in LLMs
Researchers have developed two new mechanisms - Reinforcement Learning with Metacognitive Feedback (RLMF) and Metacognitive Data Selection - to improve the performance of Language Models (LLMs). The goal is to make…
TreeAgent: A Generalizable Multi-Agent Framework for Automated Bias Labeling in Forestry via Compiled Expert Rules and Vision-Language Models
Researchers have proposed a multi-agent system that uses expert decision trees and Vision-Language Models (VLMs) to automate the bias classification in tree height in forestry remote sensing. The system mitigates VLM…
TRIAGE: Role-Typed Credit Assignment for Agentic Reinforcement Learning
The paper proposes a new framework called TRIAGE for credit assignment in agentic reinforcement learning. This framework adds a semantic role axis to outcome credit, aiming to correct the issues of standard GRPO where…
BayesEvolve: Explicit Belief States for Autonomous Scientific Discovery
The paper introduces a new framework called BayesEvolve for autonomous scientific discovery. This framework aims to improve the efficiency of experimentation by maintaining explicit, uncertainty-aware beliefs about…
DOPD: Dual On-policy Distillation
The paper proposes a new method called Dual On-policy Distillation (DOPD) for transferring knowledge from a teacher model to a student model in on-policy distillation. DOPD aims to address the issue of 'privilege…
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