Search: “llm”

ToolHazard: Scaling Adversarial Environments for Security Evaluation and Alignment of LLM-based Agents

arXiv:2608.11878v1 Announce Type: cross Abstract: Large language model (LLM) agents integrated with external tools are vulnerable to indirect prompt injections embedded in environmental states. Howeve

13 Aug 2026

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Model Releases

XBridge: Entity-Grounded Latent Bridge for Heterogeneous LLM Communication

DGX agent

arXiv:2608.11676v1 Announce Type: new Abstract: Heterogeneous multi-agent LLM systems, where agents are powered by different model families, can outperform homogeneous configurations by reducing redun

model-releasesarxiv-cs-ai

13 Aug 2026

Bayesian-Agent: Posterior-Guided Skill Evolution Across LLM Agent Harnesses

arXiv:2606.08348v2 Announce Type: replace Abstract: LLM agents increasingly rely on prompts, tools, memory, SOPs, skills, and harness feedback, yet current self-evolution pipelines often update these

Detecting an Effect Is Not Learning to Act on It: A Reward-SNR Floor for LLM Acquisition Agents

arXiv:2608.10441v1 Announce Type: cross Abstract: Many pipelines can pay a per-example cost to acquire an auxiliary, model-derived observation -- an LLM's structured reasoning, a slow oracle, an expen

FoR-SALE: Frame of Reference-guided Spatial Adjustment in LLM-based Diffusion Editing

arXiv:2509.23452v2 Announce Type: replace-cross Abstract: Current text-to-image generation models, even state-of-the-art models, exhibit a significant performance gap when spatial expressions are desc

Applications

REATS: LLM Reasoning-based Ensemble Learning for Adaptive Time Series Forecasting

arXiv:2608.10149v1 Announce Type: new Abstract: Due to the diversity of real-world time series, no single forecasting model consistently dominates across all samples. Ensemble learning addresses this

applicationsarxiv-cs-lg

Reinforcement Learning-based Semi-supervised Knowledge Distillation with LLM-as-a-Judge

arXiv:2604.02621v2 Announce Type: replace Abstract: Reinforcement Learning (RL) substantially improves the reasoning capabilities of language models, but most existing RL fine-tuning approaches rely e

researcharxiv-cs-cl

ReRound: Reconstructive Rounding to Resolve Midpoint Ambiguity in Calibration-Free LLM Quantization

arXiv:2608.11045v1 Announce Type: cross Abstract: ReRound (Reconstructive Rounding) is a post-training quantization method that addresses the midpoint ambiguity inherent in standard round-to-nearest (

When Do Anchor-Based Pointwise LLM Rerankers Help? Retriever Quality, Statistical Scope, and Anchor Design

arXiv:2608.10528v1 Announce Type: cross Abstract: Anchor-based pointwise LLM reranking scores each candidate against a shared reference passage to recover cross-document context at pointwise cost. We

researcharxiv-cs-lg

'YES! YES! I absolutely love this insight!' Affirmative Narration as Interactional Strategy in Dialogues with LLM Chatbots

arXiv:2607.28646v2 Announce Type: cross Abstract: This article analyses narrative mechanisms that are common in dialogues with LLM chatbots. In combination, these mechanisms produce an interactional s

Autorubric: A Unifying Framework for Rubric-Based LLM Evaluation on Non-Verifiable Tasks

arXiv:2603.00077v3 Announce Type: replace-cross Abstract: Rubric-based LLM judges have become indispensable for evaluating and optimizing systems on non-verifiable tasks, where success cannot be reduc

Beyond the Capability Boundary: Zeroth-Order Optimization for Self-Evolving LLM Agents

arXiv:2608.09292v1 Announce Type: cross Abstract: Self-evolving methods improve the capabilities of LLM agents by sampling trajectories from the underlying LLMs and learning from these trajectories. H

Bidirectional Context Self-Distillation for Reinforcement Learning of Skill-Based LLM Agents

arXiv:2608.09555v1 Announce Type: new Abstract: External natural-language skills provide large language model (LLM) agents with reusable and editable guidance for solving complex tasks. Yet their effe

Fair on the Surface? Benchmarking Hidden-Output Fairness Gaps in LLM Recommenders

arXiv:2608.08284v1 Announce Type: new Abstract: Fairness audits for LLM-based recommenders have largely focused on observable outputs, implicitly assuming that stable recommendations reflect stable in

Janus: An Algorithm-Evaluator Co-Evolution Framework for LLM-Driven Discovery under Expensive Evaluation Budgets

arXiv:2608.08189v1 Announce Type: new Abstract: LLM-driven program discovery relies on rapid evaluator feedback, but many scientific and engineering tasks require high-fidelity simulations, hardware e

On the Effect of Sampling Diversity in Scaling LLM Inference

arXiv:2502.11027v5 Announce Type: replace Abstract: Large language model (LLM) scaling inference is key to unlocking greater performance, and leveraging diversity has proven an effective way to enhanc

researcharxiv-cs-lg

Privacy-Preserving Data Drift Detection and Recovery for Large-Scale LLM Applications via Proxy Representations

arXiv:2608.08245v1 Announce Type: cross Abstract: LLM applications deployed at scale face a fundamental challenge: privacy constraints prevent direct inspection of user interactions, making it difficu

GPTKB 2.0: Browsing, Querying, and Auditing a Disambiguated LLM-Derived Knowledge Base

arXiv:2608.06992v1 Announce Type: cross Abstract: We present a web demo for exploring a large-scale disambiguated knowledge base (KB) materialized from a large language model (LLM). GPTKB 2.0 contains

10 Aug 2026

Shape Your Feed: An LLM-based Agentic System for Conversational Recommendation

arXiv:2608.06632v1 Announce Type: new Abstract: Industrial recommendation systems predominantly adopt a passive ranking paradigm that infers user preferences from implicit behavioral signals (e.g., cl

10 Aug 2026

[2606.05682] Beyond Output Matching: Preserving Internal Geometry in NVFP4 LLM Distillation

model-releasesr-localllama

Demand for low-precision inference, including NVFP4-based approaches, has grown as large language models are increasingly deployed in latency and cost constrained production environments. Quantization

9 Aug 2026

Okay hear me out new IQ test: see if an LLM gets smarter or dumb after fine-tuning on your stream of consciousness

Simon @disiok proposed a novel IQ test for language models, asking whether an LLM becomes smarter or dumber after being fine‑tuned on a user’s stream of consciousness. The tweet was posted on 9 Aug 20

agentsjerry-liu--x

9 Aug 2026

Tools

Autoscaling peaky LLM inference workloads is completely different than autoscaling something like a web service. I wrote a deepdive covering…

Zain (@zainhas) published a detailed article on August 7, 2026 explaining that autoscaling for highly peaky large‑language‑model (LLM) inference is fundamentally different from autoscaling conventiona

toolstogether-ai--x

DreamGuard: Efficient Runtime Guardrail for LLM Agents via Risk-Aware World Model

arXiv:2608.05695v1 Announce Type: new Abstract: As large language model (LLM) agents increasingly invoke external tools and interact with real-world systems, unsafe actions may cause irreversible cons

EvoHarness-RL: Learning Self-Evolving Runtime Harness for Long-Horizon LLM Agents

arXiv:2608.05446v1 Announce Type: cross Abstract: Long-horizon LLM agents increasingly rely on external execution support to maintain state, track progress, invoke tools, verify outcomes, and reuse ex

Hyper-ES: Effective Evolution Strategies for LLM Reasoning via Descent Direction Merging

arXiv:2608.05541v1 Announce Type: new Abstract: Evolution Strategy (ES) is a promising alternative to gradient-based fine-tuning for resource-constrained Large Language Model (LLM) reasoning. However,

Relay, Don't Route: Adaptive Population Handoff for Cost-Efficient LLM-Driven Evolution

arXiv:2608.05651v1 Announce Type: cross Abstract: Large language model (LLM)-driven evolution has shown promise for program search and algorithm discovery, but relying on strong models throughout long

AgentAntibody: An Adaptive Immune System for Defending LLM Agents against Prompt Injection

arXiv:2608.04053v1 Announce Type: cross Abstract: Prompt injection remains a critical threat to LLM agents, yet existing defenses treat each task as a self-contained problem, independent of previous e

EASy: Towards Efficient LLM-Based Agentic System

arXiv:2608.04588v1 Announce Type: cross Abstract: Agentic systems have emerged as a promising paradigm for solving complex tasks by coordinating specialized LLM-based agents. However, most existing sy

Feedback Loops and Code Perturbations in LLM-based Software Engineering: A Case Study on a C-to-Rust Translation System

arXiv:2512.02567v2 Announce Type: replace-cross Abstract: The advent of strong generative AI has a considerable impact on various software engineering tasks such as code repair, test generation, or la

Applications

MIDAS: Multi-LLM Iterative Data-Adaptive Summarization

arXiv:2608.04307v1 Announce Type: cross Abstract: Text summarization is deceptively difficult. While condensing information seems straightforward, real-world enterprise summarization of support ticket

applicationsarxiv-cs-ai

Strategic Evaluation of Planning Strategies for LLM Agents in Cyber-Physical Systems

arXiv:2608.04265v1 Announce Type: cross Abstract: Evaluations of LLM planning agents largely ask whether a task succeeds or a declared plan is followed. In strategic cyber-physical systems, a stronger

The LLM Proposes, the Executive Disposes: A Self-Verifying Agent Instrument that Dissociates Commitment Drift from Binding Drift in Long-Horizon Agents

arXiv:2608.04066v1 Announce Type: new Abstract: How do you verify a long-horizon agent when its own state and self-reports are exactly what you cannot trust? We present an agent instrument built so th

DiagChain: A Diagnostic Benchmark for Evaluating LLM Agents on Evidence-Grounded Attack Chain Reconstruction

arXiv:2608.03591v1 Announce Type: cross Abstract: Large Language Model (LLM) agents offer a promising approach to attack chain reconstruction by retrieving and interpreting heterogeneous telemetry to

DP-MemView: A Memory Interface for Attribute-Level Transcript Privacy in Long-Term LLM Agents

arXiv:2608.03130v1 Announce Type: cross Abstract: Long-term memory enables persistent personalization in LLM agents, but repeated memory-conditioned responses can cumulatively reveal protected attribu

LatentGuard: Efficient and Inspectable Latent Reasoning for LLM Safeguards

arXiv:2608.03838v1 Announce Type: new Abstract: Reasoning-based guard models improve LLM safeguards, but decoding explicit rationales for every interaction makes them costly to deploy. Although latent

Relational Priors as Convergence Pressure in LLM-Based Multi-Agent Systems

arXiv:2608.03239v1 Announce Type: new Abstract: Large language model-based multi-agent systems (LLM-MAS) are designed through roles, debate protocols, and aggregation rules. These choices create impli

SparSEEty: Extracting Tokens from Sparsity-Exploiting LLM Serving Systems via Deterministic Side Channels

arXiv:2608.02995v1 Announce Type: cross Abstract: Modern large language models (LLMs) exhibit activation sparsity, wherein only a subset of their neurons is activated for given input tokens. Researche

Verified Tool Calls Improve LLM Agent Reliability Under Non-Atomic Failures

arXiv:2608.02645v1 Announce Type: cross Abstract: Large Language Model (LLM) agents rely on external tools to perform multistage tasks. Existing agent frameworks typically assume that tool calls are a

Cooperative Coevolution for Resource-Constrained Agentic LLM Post-Training

model-releasesarxiv-cs-lg

arXiv:2608.02391v1 Announce Type: cross Abstract: Tool-using large language model (LLM) agents produce long, multi-turn trajectories, making gradient-based post-training memory-intensive. Evolution st

4 Aug 2026

Passing Coarse Marginal Checks Can Be Cheap: Persona Mixtures and Imprecise Treatment-Response Estimates in an LLM Persona Panel

arXiv:2608.00979v1 Announce Type: cross Abstract: Large language models are increasingly used as synthetic research participants and are often validated by whether their marginal responses resemble hu

4 Aug 2026

Real-Time Detection and Repair of LLM Agent Failures

model-releasesarxiv-cs-lg

arXiv:2608.02464v1 Announce Type: cross Abstract: LLM agents fail mid-episode -- they loop, cascade tool errors, drift off goal, fabricate results, or silently absorb corrupted content -- and the stan

4 Aug 2026

A robust association between LLM use and scientific productivity: Assessing stopping-time selection

arXiv:2607.28968v1 Announce Type: cross Abstract: Renault, Bergeaud, and Bosquet (hereafter RBB) argue that dating LLM adoption as the first month in which an author's abstract is flagged induces a st

Finally a good paper testing whether agent memory needs an LLM at all. Production memory stacks spend extra model calls on summarizing inter…

Finally a good paper testing whether agent memory needs an LLM at all. Production memory stacks spend extra model calls on summarizing interactions, writing records, and reranking retrievals. Every on

agentsdair-ai--x

Overcoming the Weakest-Link Effect in LLM-Driven Program Optimization via Heterogeneous Edit Recombination

arXiv:2607.28947v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to solve complex problems by searching over program space, offering a general paradigm for scientific

agentsarxiv-cs-lg

Local Ai

Reflection or Re-Generation? Why LLM Revision Fails Where Human Revision Succeeds

arXiv:2607.28908v1 Announce Type: new Abstract: Reflection, the ability to revisit and revise prior reasoning, is central to how humans improve their answers. Large language models (LLMs) are increasi

local-aiarxiv-cs-lg

To Add Is Machine, To Delete Is Human: Measuring and Mitigating Deletion Avoidance in LLM Code Editing

arXiv:2607.28887v1 Announce Type: cross Abstract: Large language models increasingly write and repair production code, yet evidence is mounting that their test-passing patches leave codebases harder t

Hardware

TokTier: Exact Stateful Tokenization for Agentic LLM Serving

arXiv:2607.29678v1 Announce Type: new Abstract: LLM serving systems cache prompt KV state, yet most front ends still re-tokenize the full request text on every call. The cost lands on coding agents, w

hardwarearxiv-cs-cl

Even More Deception: Objective Misalignment in Mixed-Motive LLM Multi-Agent Systems

arXiv:2607.26120v1 Announce Type: new Abstract: Large Language Models (LLMs)-powered multi-agent systems are increasingly deployed in mixed-motive environments, where agents operate under asymmetric i