Beyond Parallel Sampling: Diverse Query Initialization for Agentic Search
arXiv:2606.17209v1 Announce Type: new
Abstract: Test-time scaling for agentic search typically increases depth (i.e., more turns and tokens per trajectory) or breadth (i.e., more parallel rollouts). Here we focus on breadth scaling, showing that standard parallel sampling yields diminishing returns...
When Rules Learn: A Self-Evolving Agent for Legal Case Retrieval
arXiv:2606.17220v1 Announce Type: new
Abstract: Legal case retrieval remains challenging due to the complexity of legal language and the need for precise lexical alignment between queries and relevant cases. Although dense retrieval models have achieved notable progress, empirical studies show that...
SkillChain-Gym: A Benchmark for Reskilling-Aware Production-Inventory Control under Disruptions
arXiv:2606.17266v1 Announce Type: new
Abstract: Production planning increasingly has to treat workforce capability as a decision variable: certifications lapse when skills are not maintained, new products require skills the current workforce does not hold, and reskilling competes for the same worke...
Skill-Constrained Model Predictive Control for Resilient Manufacturing Supply Chains
arXiv:2606.17269v1 Announce Type: new
Abstract: In skill-constrained production-inventory systems, the qualified human capacity available tomorrow depends on training decisions made today: production requires certified workers, certifications decay unless maintained, and training consumes the same ...
Nothing from Something: Can a Language Model Discover 0?
arXiv:2606.17289v1 Announce Type: new
Abstract: AI systems based on artificial neural networks are being developed with aspirations of pushing the boundary of human mathematical knowledge. A key question for these systems is how much they can reach beyond their training data. Mathematical discovery...
QPILOTS: Efficient Test-Time Q-Steering for Flow Policies
arXiv:2606.14801v1 Announce Type: new
Abstract: Flow-matching and diffusion policies are expressive action generators, but optimizing them with temporal-difference reinforcement learning (RL) remains difficult. Effective policy extraction requires exploiting the critic's action gradient, yet direct...
GRASP: Gradient-Aligned Sequential Parameter Transfer for Memory-Efficient Multi-Source Learning
arXiv:2606.14900v1 Announce Type: new
Abstract: Multi-source transfer learning faces a fundamental scalability bottleneck: existing approaches require either loading all K source models into memory simultaneously during parameter fusion, requiring O(K) memory, or deploying all models at inference t...
Policy Regret for Embedding Model Routing: Contextual Bandits with Low-Rank Experts
arXiv:2606.14929v1 Announce Type: new
Abstract: Modern recommendation systems increasingly rely on dynamically routing diverse queries to multiple embedding models. Despite its practical significance, this problem remains poorly understood under realistic conditions like adversarial queries, bandit...
A Definition of Good Explanations and the Challenges Explaining LLM Outputs
arXiv:2606.14838v1 Announce Type: new
Abstract: How to define a good explanation is a long-standing philosophical debate which has found recent renewed interest in the context of AI outputs. Explainability is crucial for AI adoption in many contexts, but in order to produce good explanations of AI ...
Dr-DCI: Scaling Direct Corpus Interaction via Dynamic Workspace Expansion
arXiv:2606.14885v1 Announce Type: new
Abstract: Agentic search over large corpora relies on retriever-mediated interfaces (e.g., BM25 or ColBERT) for scalable candidate discovery. While effective at ranking relevant documents, these interfaces expose evidence only as ranked results or bounded docum...
arXiv:2606.14892v1 Announce Type: new
Abstract: An artificial intelligence must have a model of its environment that is causal, supporting reasoning about interventions and counterfactuals, and also combinatorial, supporting generalization to unseen combinations of objects. In this work, we formall...
Trust Between AI Agents: Measuring Formation, Breakage, and Recovery, with Implications for Governing Multi-Agent Systems
arXiv:2606.14923v1 Announce Type: new
Abstract: As language-model agents increasingly work in teams, each agent must decide how much to trust its teammates. Yet we lack a standard way to measure trust between AI agents. We propose a behavioral measure based on costly verification. In a cooperative ...
PrologMCP: A Standardized Prolog Tool Interface for LLM Agents
arXiv:2606.14935v1 Announce Type: new
Abstract: Frontier reasoning-tuned language models still fail on deductive tasks at depth, and the cost of improved performance through extended internal reasoning scales poorly. Symbolic delegation offers a complementary route: a language model translates the ...
Can Editing 1 Neuron Fix Repetition Loops in LLMs?
arXiv:2606.13705v1 Announce Type: new
Abstract: Yes. Can it cure doom loops? Probably not.
The Gemma 4 instruction-tuned models share a reproducible failure: on long factual enumeration prompts, such as listing every episode of a TV series, the 88 IAU constellations, or the 151 original Pokemon, ...
Efficient On-Device Diffusion LLM Inference with Mobile NPU
arXiv:2606.13740v1 Announce Type: new
Abstract: Diffusion large language models (dLLMs) accelerate generation by denoising multiple tokens in parallel, making them attractive for latency-sensitive mobile inference. However, repeated denoising introduces substantial computation on smartphones. Mobil...
arXiv:2606.13741v1 Announce Type: new
Abstract: This paper presents the design, development, and implementation of a specialized forecast-then-optimize algorithmic pricing tool for sales campaigns in fashion e-commerce. Sales events present unique challenges for pricing including volatile demand pa...
A fully GPU-based workflow for building physics emulators of hypersonic flows
arXiv:2606.13742v1 Announce Type: new
Abstract: The ability to resolve complex physical phenomena with high fidelity and at low computational cost is central to addressing key challenges in modern engineering. A prime example lies in hypersonic flows, where the precise prediction of the full flowfi...
FedSPC: Shared Parameter Correction for Personalized Federated Learning
arXiv:2606.13748v1 Announce Type: new
Abstract: Personalized federated learning (PFL) is one of the important approaches in federated learning for addressing statistical heterogeneity while enabling client-specific adaptation. Many PFL methods split the model into shared and personalized parameters...
A Deep Reinforcement Learning (DRL)-Based Transformer Method for Solving the Open Shop Scheduling Problem
arXiv:2606.13682v1 Announce Type: new
Abstract: The open shop scheduling problem (OSSP) arises in many industrial and service settings but remains computationally challenging as the number of jobs and machines increases. While exact methods quickly become intractable, classical dispatching rules an...
UP-NRPA: User Portrait based Nested Rollout Policy Adaptation for Planning with Large Language Models in Goal-oriented Dialogue Systems
arXiv:2606.13683v1 Announce Type: new
Abstract: To address the challenge that current dialogue policy planning methods struggle to dynamically adapt to diverse user characteristics, this paper proposes a User Portrait based Nested Rollout Policy Adaptation (UP-NRPA) online framework with Large Lang...
arXiv:2606.13707v1 Announce Type: new
Abstract: The recent success of agent swarms has shifted the paradigm of large language model (LLM)-based agents from single-agent workflows to multi-agent systems, highlighting the importance of agent orchestration for task decomposition and collaboration. How...