arXiv:2604.14512v1 Announce Type: cross Abstract: Agent communication languages (ACLs) enable heterogeneous agents to share knowledge and coordinate across diverse domains. This diversity demands extensibility, but expressive extension mechanisms can push the input language beyond the complexity classes where full validation is tractable. We present CBCL (Common Business Communication Language), an agent communication language that constrains […]
Prompt Optimization Is a Coin Flip: Diagnosing When It Helps in Compound AI Systems
arXiv:2604.14585v1 Announce Type: new Abstract: Prompt optimization in compound AI systems is statistically indistinguishable from a coin flip: across 72 optimization runs on Claude Haiku (6 methods $times$ 4 tasks $times$ 3 repeats), 49% score below zero-shot; on Amazon Nova Lite, the failure rate is even higher. Yet on one task, all six methods improve […]
Multi-Frequency Local Plasticity for Visual Representation Learning
arXiv:2604.09734v2 Announce Type: replace-cross Abstract: We study how far structured architectural bias can compensate for the absence of end-to-end gradient-based representation learning in visual recognition. Building on the VisNet tradition, we introduce a modular hierarchical framework combining: (i) fixed multi-frequency Gabor decomposition into F=7 parallel streams; (ii) within-stream competitive learning with Hebbian and Oja updates […]
CoDaS: AI Co-Data-Scientist for Biomarker Discovery via Wearable Sensors
arXiv:2604.14615v1 Announce Type: new Abstract: Scientific discovery in digital health requires converting continuous physiological signals from wearable devices into clinically actionable biomarkers. We introduce CoDaS (AI Co-Data-Scientist), a multi-agent system that structures biomarker discovery as an iterative process combining hypothesis generation, statistical analysis, adversarial validation, and literature-grounded reasoning with human oversight using large-scale wearable datasets. […]
NewsTorch: A PyTorch-based Toolkit for Learner-oriented News Recommendation
arXiv:2604.14510v1 Announce Type: cross Abstract: News recommender systems are devised to alleviate the information overload, attracting more and more researchers’ attention in recent years. The lack of a dedicated learner-oriented news recommendation toolkit hinders the advancement of research in news recommendation. We propose a PyTorch-based news recommendation toolkit called NewsTorch, developed to support learners in […]
AgentGA: Evolving Code Solutions in Agent-Seed Space
arXiv:2604.14655v1 Announce Type: new Abstract: We present AgentGA, a framework that evolves autonomous code-generation runs by optimizing the agent seed: the task prompt plus optional parent archives that initialize a fresh workspace. The outer loop searches over these reusable starting conditions rather than editing code directly. Each generation launches a fresh autonomous run from a […]
Large Language Model Post-Training: A Unified View of Off-Policy and On-Policy Learning
arXiv:2604.07941v2 Announce Type: replace-cross Abstract: Post-training has become central to turning pretrained large language models (LLMs) into aligned, capable, and deployable systems. Recent progress spans supervised fine-tuning (SFT), preference optimization, reinforcement learning (RL), process supervision, verifier-guided methods, distillation, and multi-stage pipelines. Yet these methods are often discussed in fragmented ways, organized by labels or objectives […]
M2-PALE: A Framework for Explaining Multi-Agent MCTS–Minimax Hybrids via Process Mining and LLMs
arXiv:2604.14687v1 Announce Type: new Abstract: Monte-Carlo Tree Search (MCTS) is a fundamental sampling-based search algorithm widely used for online planning in sequential decision-making domains. Despite its success in driving recent advances in artificial intelligence, understanding the behavior of MCTS agents remains a challenge for both developers and users. This difficulty stems from the complex search […]
On the Expressive Power and Limitations of Multi-Layer SSMs
arXiv:2604.14501v1 Announce Type: cross Abstract: We study the expressive power and limitations of multi-layer state-space models (SSMs). First, we show that multi-layer SSMs face fundamental limitations in compositional tasks, revealing an inherent gap between SSMs and streaming models. Then, we examine the role of chain-of-thought (CoT), showing that offline CoT does not fundamentally increase the […]
HWE-Bench: Benchmarking LLM Agents on Real-World Hardware Bug Repair Tasks
arXiv:2604.14709v1 Announce Type: new Abstract: Existing benchmarks for hardware design primarily evaluate Large Language Models (LLMs) on isolated, component-level tasks such as generating HDL modules from specifications, leaving repository-scale evaluation unaddressed. We introduce HWE-Bench, the first large-scale, repository-level benchmark for evaluating LLM agents on real-world hardware bug repair tasks. HWE-Bench comprises 417 task instances derived […]
AgentOpt v0.1 Technical Report: Client-Side Optimization for LLM-Based Agent
arXiv:2604.06296v2 Announce Type: replace-cross Abstract: AI agents are increasingly deployed in real-world applications, including systems such as Manus, OpenClaw, and coding agents. Existing research has primarily focused on server-side efficiency, proposing methods such as caching, speculative execution, traffic scheduling, and load balancing to reduce the cost of serving agentic workloads. However, as users increasingly construct […]
Personalized and Context-Aware Transformer Models for Predicting Post-Intervention Physiological Responses from Wearable Sensor Data
arXiv:2604.14738v1 Announce Type: new Abstract: Consumer wearables enable continuous measurement of physiological data related to stress and recovery, but turning these streams into actionable, personalized stress-management recommendations remains a challenge. In practice, users often do not know how a given intervention, defined as an activity intended to reduce stress, will affect heart rate (HR), heart […]