Search: “llm”

Validated Intent Compilation for Constrained Routing in LEO Mega-Constellations

arXiv:2604.07264v1 Announce Type: cross Abstract: Operating LEO mega-constellations requires translating high-level operator intents ('reroute financial traffic away from polar links under 80 ms') int

10 Apr 2026

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Local Ai

I built a tiny site that let's you feel the tokens/s before you buy the GPU

DGX agent

I made a tiny site that let's you feel how fast a local LLM runs before buying the hardware. I made it for myself and a friend but thought it could be useful for others. Note that it works as an estim

local-air-localllama

17 Oct 2026

What do you guys use for chat interface?

model-releasesr-localllama

I use Pi/OpenCode for coding tasks which still seems to be the most popular, but I'm wondering what you guys use for normal chat interface. It's been a few months since I've seen discussions about har

17 Oct 2026

BASIS: Evaluating Belief-Aware Solutions in Interactive Settings beyond Instructions

arXiv:2602.13832v2 Announce Type: replace Abstract: Large language model (LLM) agents are increasingly capable of carrying out complex, long-horizon tasks, yet effective assistance requires more than

Bias Control and Injection for Multi-Agent Aggregation

arXiv:2610.21290v1 Announce Type: new Abstract: Multi-agent reasoning with large language models (LLMs) raises a central question for collective decision-making: when agents disagree, whose decision s

BitSparse: Compact Activation Storage for ReLU Feed-Forward Networks

model-releasesarxiv-cs-lg

arXiv:2610.22256v1 Announce Type: new Abstract: Reducing memory use in large language model (LLM) training is an ongoing challenge. Activation checkpointing saves memory by recomputation, while lossy

CARE-SD: Explainable Reasoning Architecture for Local SLM Self-Disclosure Analysis in Textual Data

arXiv:2610.21151v1 Announce Type: cross Abstract: Cybersecurity interviews uncover critical security experiences, behaviors, and privacy concerns often not fully captured by conventional technical ass

Close Enough is Nowhere Near: Fidelity-Consumability Decoupling in KV Caches

arXiv:2610.22016v1 Announce Type: new Abstract: Multi-agent LLM systems communicate in text, which is lossy and wasteful: the sender throws away the reading it already did. A growing literature instea

agentsarxiv-cs-lg

DeciCAD: Harness-Grounded Learning of Action Policies for CAD Agents

arXiv:2610.21755v1 Announce Type: new Abstract: Large Language Model agents operate through closed-loop action policies whose decision space is defined by an agent harness. We investigate how this ope

Four evaluation engineering lessons learned from building agent plugins

model-releasesgoogle-cloud-ai

The real challenge of building an agentic system isn't the construction, it's the measurement. Evaluation engineering is a critical foundation for building reliable AI systems. Setting up systematic,

HTN-Orchestrate: A Hierarchical Task Network Framework for Multi-Agent Orchestration

arXiv:2610.22195v1 Announce Type: new Abstract: Orchestrating multiple agents on a complex task requires reconciling local subtask execution with global task planning. Dynamic dispatch approaches prov

IneqAgent: Agentic Inequality Proving in Lean with Symbolic Computation

arXiv:2610.20681v1 Announce Type: new Abstract: Formal inequality proving requires mathematical reasoning and exact computation, often connected through intermediate lemmas and goal reformulations. We

Research

Language Models Share a Readout Bottleneck in Working Memory

arXiv:2610.21176v1 Announce Type: cross Abstract: Large language models (LLMs) reproduce a human working-memory signature on the N-back task: performance declines as memory load N increases, and error

researcharxiv-cs-ai

Large Language Models Naively Recover Ethnicity from Individual Records

arXiv:2601.21132v2 Announce Type: replace Abstract: I demonstrate that large language models can infer ethnicity from names with accuracy exceeding that of Bayesian Improved Surname Geocoding (BISG) w

MemScope: Evolving Agent Memory Architectures through Localized Code Repair

arXiv:2610.21170v1 Announce Type: new Abstract: Agent memory architectures define how LLM agents store and reuse experience across tasks, but they are typically fixed after deployment, limiting their

Multi-Reward Hacking: From Aggregation-Induced Drift to Trust-Anchored Defense

arXiv:2610.21404v1 Announce Type: cross Abstract: To improve task performance while encouraging desirable response properties, LLM post-training often combines a primary reward (e.g., correctness) wit

OntoAct: Hypothesis-Based Neuro-Symbolic Agents through Ontological Abstraction

arXiv:2610.21983v1 Announce Type: new Abstract: Learning from experience requires recovering reusable knowledge and determining when it applies to a new situation. We introduce OntoAct, a hypothesis-b

Prune the Summary, Not the History: Source-Judged Compaction for Long-Horizon Agents

arXiv:2610.20927v1 Announce Type: new Abstract: Long-horizon LLM agents rely on context compaction: when the interaction history outgrows the context window, a compressor replaces it with a natural-la

ReviewPilot: A Multi-Agent System for Screening and Data Extraction in Systematic Reviews

arXiv:2610.21042v1 Announce Type: cross Abstract: Systematic literature reviews follow the PRISMA process of screening scholarly works and extracting evidence from included studies. Deep research tool

agentsarxiv-cs-ai

RSIFlow: Adaptive Self-Improvement Workflows with an Evolving Meta-Agent

arXiv:2610.21791v1 Announce Type: new Abstract: Large language model (LLM) agents have demonstrated strong capabilities in complex tasks, including code generation, tool use, and multi-hop question an

agentsarxiv-cs-ai

SCARCE: Emergent Institutions in Long-Horizon Multi-Agent Coding Systems

arXiv:2610.21029v1 Announce Type: cross Abstract: As autonomous agents become widely deployed, coordinating access to shared computing resources and services will become increasingly important. Recent

SkillNEO: Non-Elitist Evolutionary Optimization of Agent Skills

arXiv:2610.20926v1 Announce Type: new Abstract: Agent skills turn procedural knowledge into reusable artifacts, allowing LLM agents to accumulate and refine expertise through experience. Building on t

StepSafe: Learning Timely Intervention with Context-Aware Safety Reasoning

arXiv:2610.20960v1 Announce Type: new Abstract: Safety in agentic systems is inherently sequential: whether an action is safe depends on prior actions, tool outputs, information disclosures, and inter

The Delegation Dilemma: Economies of Scale, Accountability, and Polycentric Governance

arXiv:2610.21746v1 Announce Type: cross Abstract: Many collective-action systems are neither fully decentralized nor fully centralized: agents voluntarily organize into institutions that share organiz

VALCON: Benchmarking and Enhancing Value Consistency in Large Language Models via Dual-Role Variation

arXiv:2610.21304v1 Announce Type: new Abstract: Current value alignment evaluations for large language models (LLMs) heavily rely on output compliance, overlooking whether LLMs maintain value consiste

Hardware

When the KV Cache Is Slower Than the GPU: Scaling Remote KV Tiers for Context-Parallel Prefill

arXiv:2610.21500v1 Announce Type: cross Abstract: Reusing the attention KV cache across requests is a standard way to cut prefill cost in long-context LLM serving, and production systems increasingly

hardwarearxiv-cs-lg

Witness Overlap: Directional Provenance Inside Open-Weight Model Families

arXiv:2609.31784v2 Announce Type: replace Abstract: Open-weight models are often released, fine-tuned, aligned, merged, and re-released, making provenance audits ask not only whether checkpoints are r

Are Benchmarks Reliable? Toward Structural Diagnosis via Item-Level Capability Boundaries

arXiv:2609.33196v2 Announce Type: replace Abstract: Evaluating large language models (LLMs) relies heavily on benchmark scores, yet aggregate metrics can obscure whether individual items serve their i

Hardware

AReaL-RSI: Adaptive Scaling and Coordination of Recursive Self-Improvement

arXiv:2610.18083v1 Announce Type: new Abstract: Scaling recursive self-improvement requires additional computation to produce useful research progress in reaching persistent objectives. Intuitively, m

hardwarearxiv-cs-ai

AtomSearch: Evidence-Grounded Tree Search for Agent Skill Optimization

arXiv:2610.19951v1 Announce Type: new Abstract: Agent skills adapt frozen models through editable external instructions. Revising several rules at once makes changes difficult to inspect, while judgin

Beyond Outcome Feedback: Trajectory-Quality-Guided Experience-based Learning

arXiv:2610.14877v1 Announce Type: new Abstract: Experience-based learning enables large language model (LLM) agents to improve over time by reusing knowledge from past executions. However, existing me

Local Ai

Calibration and Risk-Budgeted Decisions with System One Models

arXiv:2610.19811v1 Announce Type: cross Abstract: LLM workflows and agents rely on a set of binary decisions like allow/block an input, or discard/retain a retrieved document. When human deferral is u

local-aiarxiv-cs-lg

ChemArgus: Automated Scientific Diagnostics for Lab-Grade Multimodal Chemistry Reasoning: A Frontier Benchmark

model-releasesarxiv-cs-lg

arXiv:2610.20012v1 Announce Type: new Abstract: Chemistry evaluation has moved past multiple-choice probes toward open-ended expert problems, yet the benchmarks that define the current frontier still

Tutorials

Configuring RAG Systems: What Breaks, Where Originates, and How to Fix

arXiv:2610.18567v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has emerged as a dominant paradigm for improving Large Language Model (LLM) by intertwining it with other component

tutorialsarxiv-cs-ai

Counterfactual Regret for Reflective Agents with Lean-Verified Guarantees

arXiv:2610.14790v1 Announce Type: new Abstract: Reflective language agents reuse feedback and memory to adapt across tasks without updating model weights. However, existing guarantees for policy selec

Tutorials

Decoupled Marginal Sharpening for Training-Free Inference-Time Scaling

arXiv:2610.20483v1 Announce Type: new Abstract: We study how to improve LLM reasoning at inference time without updating model weights or using external reward models. Recent reward-free power-samplin

tutorialsarxiv-cs-lg

Delta Tokenizer: Reference-Identical Tokenization of Session Deltas and Segment-Parallelism for Long Prompts on Multicore CPUs

arXiv:2610.19801v1 Announce Type: new Abstract: An agent that drives an LLM sends the model the same conversation again and again, and each turn adds only a little text at the end. Inference engines r

agentsarxiv-cs-cl

DivAlgo: A systematic evaluation of algorithmic diversity for code generation models

arXiv:2610.19157v1 Announce Type: cross Abstract: Code generation models are actively being used in professional and educational settings. However, the competence of large language models (LLMs) to ge

Do Perturbation Predictors Learn Gene Interactions? Diagnosing Extrapolation in Agentic Screening

arXiv:2610.18880v1 Announce Type: new Abstract: High-throughput perturbation screens increasingly use machine-learning models to predict the effects of gene combinations that were never measured. A di

agentsarxiv-cs-lg

EGCForest: Structured Agent Memory Organization for Enterprise Group Chats

arXiv:2610.19836v1 Announce Type: new Abstract: Enterprise group chats record project progress, decisions, task ownership, and other organizational information valuable to enterprise agents. Reliable

agentsarxiv-cs-cl

Energy Consumption Analysis of Discrete Diffusion and Autoregressive Language Models

model-releasesarxiv-cs-lg

arXiv:2610.19855v1 Announce Type: new Abstract: As Large Language Models (LLMs) continue to scale, their energy consumption during inference has become a critical concern. While Autoregressive (AR) mo

Evolve Together, Fall into Place: How Social Scaffolding Shapes Self-Evolving Multi-Agent Systems

arXiv:2610.19799v1 Announce Type: new Abstract: Recent work lets LLM agents evolve their own harness, up to the team itself. Yet in human groups, the same abilities achieve different outcomes under di

agentsarxiv-cs-cl

EvoQuantLab: Training-Free Dual-Loop Evolution for Auditable Quantitative Research

arXiv:2610.17130v2 Announce Type: new Abstract: Automated alpha factor discovery is critical for modern quantitative research. While large language models (LLMs) have increasingly been integrated into

agentsarxiv-cs-ai

Explorer-Zero: Improving Self-Play for Long-Horizon Tasks with Decomposable Verifiable Rewards

arXiv:2610.19868v1 Announce Type: cross Abstract: Training LLM agents on long-horizon interactive tasks with reinforcement learning typically relies on human-annotated tasks and reliable verification,

agentsarxiv-cs-lg

FOLD: Position-Free Far Memory with Local Order for Long-Context Speculative Decoding

arXiv:2610.20392v1 Announce Type: new Abstract: Speculative decoding accelerates LLM generation through lightweight drafting and parallel verification. It is particularly promising for long inputs, wh

Research

From Caricature to Collective Artifact: How Persona Construction Drives Polarization in Generative Social Simulations

arXiv:2610.16498v1 Announce Type: cross Abstract: Generative Social Simulation (GSS) leverages Large Language Models (LLMs) to create sophisticated personas that define agents with demographics, emoti

researcharxiv-cs-ai

Applications

Generating Synthetic Doctor-Patient Conversations for Long-form Audio Summarization

arXiv:2604.06138v2 Announce Type: replace-cross Abstract: Long-context audio reasoning is underserved in both training data and evaluation. Existing benchmarks target short-context tasks, and the open

applicationsarxiv-cs-ai

Research

HERO: Harnessing Evidence for Test-time Refinement and Prompt Optimization in Program Generation

arXiv:2610.19184v1 Announce Type: new Abstract: Harness optimization improves LLMs by refining reusable prompts and scaffolding, yet requires further adaptation for program generation tasks such as te

researcharxiv-cs-cl