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Notes on machine learning
235 notes, page 4 of 10
Scores Are Not Decisions: Cost-Aware Stopping for Tool Acquisition in LLM Agents
The paper presents a new approach, Cost-Aware Marginal Decision-Focused Stopping (CAM-DF), for tool selection in Large Language Model (LLM) agents that use various external services. The approach addresses the…
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…
DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data
The paper proposes a framework called DataOrchestra that learns to curate pre-training data example-by-example rather than applying a uniform strategy across many examples. The framework decides whether to drop, clean…
ERUnderstand: Evaluating Vision-Language Models on Structured ER Diagrams
A new benchmark called ERUnderstand has been introduced for evaluating the ability of Vision-Language Models (VLMs) to understand Entity-Relationship Diagrams (ERDs), which are important in database design. The…
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…
The Physics of Multi-Turn Long-Horizon Planning: From Pre-training to Post-training via Single- and Multi-Teacher On-Policy Agentic Distillation
The paper focuses on improving the multi-turn long-horizon planning for foundation model agents. The authors introduce a controlled environment to study the acquisition, shaping, and integration of planning abilities…
Agent-Guided Relational Concept Discovery: Toward Interpretable Surgical Margin Assessment
Researchers propose a new framework called Agent-Guided Concept Discovery for improving deep learning models used in surgical margin assessment. The model learns meaningful concepts directly from data without requiring…
Agentic coding without the cloud: evaluating open-weight large language models on longitudinal data preparation tasks
Researchers introduced 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 study…
AI Assistants Overassist
Researchers introduce Int-Bench, a tool to evaluate how AI language models intervene during problem-solving tasks across code debugging, mathematics, and brain teasers. The study finds that AI models provide more…
AREX: Towards a Recursively Self-Improving Agent for Deep Research
The paper introduces a new family of agents called AREX for deep research. These agents are designed to recursively improve their answers by verifying intermediate results, guiding subsequent refinement, and learning…
GRADRAG: Cross-Component Prompt Adaptation for Coordinated Multi-Agent RAG
The authors introduce GRADRAG, a framework for improving Retrieval-Augmented Generation (RAG) systems by coordinating improvements across multiple agents in the pipeline, rather than optimizing components in isolation…
GraphVid: Interactive Graph-Controllable Video Generation
The researchers introduce GraphVid, a model that generates interactive videos using structured interaction graphs, allowing for flexible and precise control. The model shows strong performance compared to Motion-I2V in…
ICAE-Bench: Evaluating Coding Agents as Interactive Project Builders
The paper introduces ICAE-Bench, a benchmark for evaluating coding agents in interactive project building settings. Instead of being limited to completing code under fully specified instructions, these agents are…
OpenForgeRL: Train Harness-native Agents in Any Environment
OpenForgeRL is an open-source framework for training AI agents using various inference harnesses like Claude Code, Codex, OpenClaw, etc., in diverse environments. The framework allows end-to-end training with standard…
Toward Continuous Assurance for the Democratization of AI Agent Creation in Industry
The paper proposes a solution to ensure reliability in AI agents created by non-engineering users within organizations, through democratized low-code, no-code, and conversational development environments. The proposed…
Antigen-specific Antibody Multi-modal Foundation Model for Functional Antibody Design
Researchers introduce a new model called AAMFM (Antigen-specific Antibody Multimodal Foundation Model) for designing functional antibodies. The model learns unified representations of antibody sequences and structures…
Autonomous Collaborative Learning Among an Ensemble of Tsetlin Machines with Consensus-Based Inference
The researchers propose a decentralized collaborative learning paradigm for an ensemble of Tsetlin Machines (TMs) using consensus-based inference. Each agent has its own private TM model, and they combine individual…
Cumsum-Composable Phase Transport for Low-Cost Streaming Keyword Spotting
The paper introduces Cumsum-Composable Phase Transport, a new streaming-native temporal layer for keyword spotting in speech tasks. This method uses unitary rotations to project and transport acoustic frames…
Multi-stage Dynamic Selection for Cross-Project Defect Prediction
The paper proposes a new framework for Cross-Project Defect Prediction (CPDP) that addresses distribution shift issues between training and target projects by using a two-stage multiple classifier system selection…
OLEDLM: A Unified Language Model for OLED Molecular Design
Researchers propose a new language model for designing Organic Light-Emitting Diode (OLED) materials. The model generates SMILES sequences of OLED molecules based on specific optoelectronic properties, using a…
Plausibility-Driven Prioritization of Candidate Biomedical Annotations
The paper presents a framework using biomedical knowledge graphs (bioKGs) to estimate the plausibility of candidate biomedical annotations, aiming to guide experts in prioritizing them for manual curation. The approach…
Variance-reduced Domain Adaptation using Paired Sampling
The paper proposes Paired Sampling for Domain Adaptation (PSDA), a new stochastic variance reduction technique designed for distribution-matching frameworks in unsupervised domain adaptation (UDA) that suffer from high…
Find Before You Fine-Tune: A Diagnostic Study of Small LLMs for Cybersecurity QA
The paper presents a diagnostic framework called FiT (Find before Fine-Tune) to evaluate the suitability of small language models for cybersecurity question-answering tasks. The study finds that fine-tuning these…
PLAID-PRF: Pseudo-Relevance Feedback with Centroid-like Tokens in PLAID
Researchers propose a method called PLAID-PRF to improve the effectiveness of multi-vector dense retrieval models like ColBERT. This method uses centroid-based quantization in PLAID, a model that reduces index size and…
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