Towards Data Science
Jul 30
How to Organize All of Your Coding Agent Tasks
Optimise how you interact with your coding agents The post How to Organize All of Your Coding Agent Tasks appeared first on Towards Data Science .
AI agents
Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
Hugging Face Trending Papers
Jul 30
One Human, $N$ Agents: Audit-Budget Allocation for LLM Agent Fleets under Miscalibrated, Correlated Confidence
A single human must audit $N$ LLM agents under a budget of $B \ll N$ audits per round, guided by self-reported confidence that may be adversarially miscalibrated and by correlated errors. We model this as budgeted noisy inspection over a two-level Gaussian copula and locate the miscalibration threshold $δ^*$ past which confidence-ranked auditing is \emph{worse} than random.
Hugging Face Trending Papers
Jul 30
Agentic Method for Deterministic Validation of Legacy Code Migration
Migration of legacy COBOL programs to Java requires extensive testing to ensure correct functionality. This effort is often complicated by the lack of test data and the difficulty of validating all corner cases.
Hugging Face Trending Papers
Jul 30
Fidelity Is Not Safety: Gently-Compressed LLMs Pass Every Data-Free Quality Guard Yet Invent Procedure Steps in Agentic Execution
Practitioners accept a compressed language model once it clears a stack of data-cheap quality guards: perplexity within a small factor of the original, downstream accuracy (for example MMLU) inside a confidence interval, and data-free output-fidelity signals that compare the compressed and original network's internal representations under random probe inputs. This stack has a blind spot.
Hugging Face Trending Papers
Jul 30
Distilling Answer Set Programming Theories from Large Language Models
Writing Answer Set Programming (ASP) theories from scratch is a difficult and time-consuming task. We take a neurosymbolic approach to study whether a model can distill complete and correct theories, given a fixed agent harness with the solver in the loop.
Hugging Face Trending Papers
Jul 30
Echoverse: Deep, Evolving Environments for Training Computer-Use Agents at Scale
Computer-use agents learn from what their actions change, so training one needs applications it can act on, break and reset. The applications that matter most are login-gated and stateful, so synthetic environments stand in for them.
Hugging Face Trending Papers
Jul 30
Contrastive Reinforced Policy Optimization via Privileged Self-Distillation
Recent advances in post-training Large Language Models (LLMs) increasingly rely on Reinforcement Learning with Verifiable Rewards (RLVR) or On-Policy Self-Distillation (OPSD). While OPSD provides dense, logit-level supervision, it inherently suffers from exposure bias due to the privileged information of the self-teacher.
Hugging Face Trending Papers
Jul 30
Share the Judge, Learn the Deferral: Where Specialization Helps LLM Evaluation
Agentic systems have widened the gap between producing candidate outputs and reviewing them. This paper asks a practical architectural question: should domain specialization be built into an evaluator's weights, or into the rule that decides when its judgment can be trusted?
Hugging Face Trending Papers
Jul 30
MARS-RA: Rank Aggregation for Credit Assignment via Multimodal Comparisons in Embodied Multi-Agent Cooperation
Credit assignment is a fundamental challenge in cooperative multi-agent reinforcement learning, particularly in embodied AI settings characterized by limited and delayed feedback as well as dynamically changing numbers of active agents. We propose MARS-RA, a framework that reformulates credit assignment as a rank aggregation problem using contribution-based pairwise comparisons among agents generated by large multimodal models.
Hugging Face Trending Papers
Jul 30
RoboBRIDGE: A Modular Framework for Bridging Policies to Robust Real-World Robotic Agents
Vision-Language-Action (VLA) models have attracted growing interest as a scalable approach to robotic manipulation. While these models are effective action predictors, deploying them as robotic agents exposes critical gaps: no mechanism for failure recovery, inconsistent execution over long horizons, and limited robustness to shifts in observations, tasks, or embodiments.
Hugging Face Trending Papers
Jul 30
ChronoMem: Version Control and Semantic Rollback for Large Language Model Agent Memory
LLM agents increasingly rely on long-term memory to support multi-session interaction and personalization. However, existing agent memory systems are designed around forward-only evolution, continuously accumulating, consolidating, and overwriting knowledge, with no principled mechanism to inspect, version, or revert prior states.
Hugging Face Trending Papers
Jul 30
MECA: A Mechanism-Centered Agent for Constructing Well-Specified and Valuable Mathematical Conjectures
Automatically constructing well-specified and valuable mathematical conjectures remains a central challenge in AI-assisted mathematical discovery. Many existing open problems and conjectures are often too broad, underspecified, or difficult to connect to plausible proof or refutation strategies.
arXiv Machine Learning
Jul 30
PowerAtlas: Towards Electricity-Computing Co-Scheduling for Power Systems
arXiv:2607. 26710v1 Announce Type: new Abstract: The rapid growth of AI workloads is turning data centers into large-scale, volatile, yet spatiotemporally flexible grid loads, creating an urgent need for coordinated electricity-computing scheduling.
arXiv Machine Learning
Jul 30
Can AI agents conduct open-ended AI research? Early evidence from two case studies
arXiv:2607. 27191v1 Announce Type: cross Abstract: Forecasts of explosive AI progress hinge on AI agents automating AI research.
arXiv Machine Learning
Jul 30
MemAgent: Reshaping Long-Context LLM with Multi-Conv RL-based Memory Agent
arXiv:2507. 02259v2 Announce Type: replace-cross Abstract: Despite improvements by length extrapolation, efficient attention and memory modules, handling infinitely long documents with linear complexity without performance degradation during extrapolation remains the ultimate challenge in long-text processing.
arXiv Machine Learning
Jul 30
Try Again, Don't Look Back: Blind Resampling Outperforms Self-Repair in Small Code Models
arXiv:2607. 26117v1 Announce Type: cross Abstract: Self-repair - returning a failed program to the model together with its test output and asking for a correction - is a standard component of code agents, and is almost always evaluated against a baseline that does not retry at all.
arXiv Machine Learning
Jul 30
RAG-HAR+: Towards Cost-Efficient LLM-Based Human Activity Recognition for Edge Deployment
arXiv:2607. 26631v1 Announce Type: new Abstract: Human Activity Recognition (HAR) from wearable sensors supports applications in healthcare, rehabilitation, fitness tracking, and smart environments.
arXiv Machine Learning
Jul 30
AgentGFM: A Graph Foundation Model with Node-Agent Information-Flow Control
arXiv:2607. 26533v1 Announce Type: new Abstract: Graph Foundation Models (GFMs) aim to learn transferable knowledge from multi-domain graphs and adapt to unseen scenarios.
arXiv Machine Learning
Jul 30
Scores Are Not Decisions: Cost-Aware Stopping for Tool Acquisition in LLM Agents
arXiv:2607. 27083v1 Announce Type: new Abstract: As LLM agents increasingly depend on diverse external services such as search engines, databases, and connectors, agent harnesses face a fundamental tool-selection challenge: acquiring too few tools leaves the task under-informed, while too many adds cost, context load, and privacy exposure.
arXiv Machine Learning
Jul 30
Post-Training at the Edge of Detectability: A Game-Theoretic Approach to Fine-Tuning
arXiv:2607. 26358v1 Announce Type: new Abstract: Reinforcement learning (RL) fine-tuning is widely used in language model training to improve model performance on a target task while limiting drift from a reference policy.