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Notes on machine learning
235 notes, page 3 of 10
HarnessOpt-Bench: Evaluating LLMs at Harness Optimization
The paper introduces HarnessOpt-Bench, a benchmark for evaluating the ability of Language Models (LLMs) to optimize the harnesses surrounding them, which includes prompts, tools, control flow, memory, and orchestration…
iARCS: Iterative Agentic RL for Controllable 3D Scene Generation
The paper presents iARCS, an iterative agentic reinforcement learning framework for enhancing the performance of synthetic 3D scene generators by adapting them to meet task-specific functional constraints, such as…
QuanTiMedAI: Quantum-Enhanced Time-Series Model guided by Agentic AI for Cardiac Arrest Mortality Prediction
The researchers introduce QuanTiMedAI, a framework for predicting cardiac arrest mortality using agentic AI and quantum enhancement in time series models. The system combines an agentic large language model for…
Tytan: Interactive Neurosymbolic Construction of Analytic Semantic Schemas from Relational Data
Researchers have developed a system called TYTAN that can automatically construct an analytic semantic schema from a relational database, helping to reduce the need for manual knowledge acquisition and potentially…
A-SR: Self-Evolving Agentic LLMs for Symbolic Regression via Hierarchical Coordination
Researchers have developed a new framework called A-SR for symbolic regression. This framework uses a self-evolving agentic system that shifts control from expression edits to role-conditioned evidence views. It…
A-SR: Self-Evolving Agentic LLMs for Symbolic Regression via Hierarchical Coordination
A research paper introduces a new framework called A-SR for symbolic regression. A-SR is designed to discover closed-form equations from data and uses a self-evolving agentic system that coordinates formula discovery…
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…
Consistency-Driven Co-Evolution for Self-Supervised Cross-Representation Learning
The paper introduces CoCoEvolve, a method aimed at improving the consistency between chart, table, and code representations in AI systems. The method optimizes models based on agreement between different…
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…
InsightEmb: Learning Action-Intent Embeddings for Agentic Insight Retrieval
The paper proposes a new method called InsightEmb for learning action-intent embeddings to improve agentic insight retrieval, which helps self-improving agents make better decisions by using insights from prior…
MultiPathFormer: Towards a Foundation Model for Multipath Wireless Propagation
In the paper 'MultiPathFormer: Towards a Foundation Model for Multipath Wireless Propagation', the authors propose a new approach for training wireless foundation models. Instead of pre-training on channel tensors…
OctoLong: Mid-Training On Cross-Repository Code Contexts Enhances Long-Context Modeling
The research presents a new approach called OctoLong for creating longer contexts for language models. It uses an AST parser, a language server backend, and a package manager to retrieve code references from various…
OctoLong: Mid-Training On Cross-Repository Code Contexts Enhances Long-Context Modeling
In this research, a new pipeline called OctoLong is introduced to enhance the training of language models (LM) by providing them with long code contexts. This is done through the use of an AST parser, a language server…
OPD-V: Visual On-Policy Self-Distillation with Modality Balance
The paper introduces a new method called On-Policy Self-Distillation with Visual Modality Balance (OPD-V) to improve the visual reasoning of multimodal large language models. The authors argue that previous methods…
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…
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…
ORCA-bench: How Ready Are Language Model Agents for Oncall?
The study presents ORCA-bench, a benchmark for evaluating the performance of large language models in oncall root cause analysis (RCA). The benchmark exposes a microservice system with six days of data and 1,079 RCA…
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…
A First Look at Coding Agents' Compliance with AI Contribution Rules in Open-Source Communities
The study investigates whether AI-generated code in open-source communities complies with the established contribution rules. The researchers find that current AI models rarely read or follow the rules on their own…
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…
CoCaRS: Correlation Calibration-Based Redundancy Suppression for Heterogeneous Knowledge Distillation
The research introduces Correlation Calibration-based Redundancy Suppression (CoCaRS), a method for knowledge distillation in heterogeneous settings where teacher and student models have different architectures. The…
From Passive Video to Editable Experience: Physically Grounded Experience Synthesis for Embodied Intelligence
Jia Luo introduces Pegasus, a framework to help robots learn from human manipulation videos by translating the human demonstrations into data that can be understood by robots. The method involves creating an…
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…
SciFigQual-Bench: A Benchmark for Scientific Figure Quality Assessment with Full-Manuscript Context
Researchers propose SciFigQual-Bench, a benchmark for evaluating the quality of scientific figures in computer science papers across five dimensions. The dataset includes over 6,000 images from top conferences between…
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