Search: “retrieval”

Reasoning Models Are Accurate but Unsound on Identification

arXiv:2610.03519v1 Announce Type: new Abstract: A reasoning model asked whether a causal effect is recoverable from observational data can fail in two ways: it refuses an identifiable query or answers

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Revisiting Visual Representation Enhancement of VLMs via Kernel Canonical Correlation Analysis

DGX agent

arXiv:2610.02718v1 Announce Type: cross Abstract: Vision-language models such as CLIP exhibit strong semantic generalization, but remain limited in fine-grained visual perception. A recent work named

safetyarxiv-cs-ai

5 Oct 2026

RT-SFT: Text Style Transfer from Non-Parallel Corpora by Roundtrip Translation

arXiv:2602.15013v2 Announce Type: replace Abstract: Text style transfer (TST) is naturally a supervised task - rewrite a sentence in a target style while preserving its meaning - yet the parallel corp

SEDIMA: Cross-Run Hierarchical Insight Memory for Evolutionary Search Agents

arXiv:2610.02361v1 Announce Type: cross Abstract: Large language model (LLM)-driven evolutionary search is a powerful paradigm for automated program and algorithm discovery, yet existing systems are l

Sentry: Learning to Recover from LLM Agent Failures at Test Time

arXiv:2610.02994v1 Announce Type: cross Abstract: LLM agents often fail mid-task due to invalid tool calls, repeated actions, or poorly grounded reasoning, and learning from these failures is a path t

Single-Pass Uncertainty Heads for Claim-Level Hallucination Detection in Persian Medical Language Models

arXiv:2610.03482v1 Announce Type: new Abstract: Hallucination detection is particularly important for medical language models, but repeated-sampling approaches are expensive and existing uncertainty-h

StanceEval 2026: The Second Stance Detection Shared Task

arXiv:2610.03215v1 Announce Type: new Abstract: StanceEval 2026 is the second edition of the StanceEval shared task series on stance detection in Arabic social media text. Stance detection aims to ide

TACD: Distilling Efficient Text-to-Motion Models via Terminal Amplification Control

arXiv:2610.02867v1 Announce Type: new Abstract: Recent text-to-motion models have improved motion quality and instruction following, yet many-step denoising and large model components make deployment

Hardware

Tailoring the Quantization Space for 1-Bit KV Cache Compression

arXiv:2610.03027v1 Announce Type: cross Abstract: The key-value (KV) cache becomes a major memory bottleneck in long-context LLM inference, placing substantial pressure on memory capacity and bandwidt

hardwarearxiv-cs-ai

WakeKV: Reactive, Reversible KV Residency for Heads That Change Their Minds

model-releasesarxiv-cs-cl

arXiv:2610.02713v1 Announce Type: new Abstract: Most KV-cache compression methods classify attention heads once, either offline or during prefill, and keep this classification fixed throughout generat

Weave Forcing: Compositional Memory Routing for Interactive Long Video Generation

arXiv:2610.03510v1 Announce Type: cross Abstract: Recent advances in autoregressive video generation have improved temporal consistency over extended durations, yet interactive storytelling requires m

poorman inference engine for 16GB GPU and 35B moe Qwen 3.6for coding

model-releasesr-localllama

I forked llama.cpp's server into AgrillaMoE, a dedicated build for Qwen3.6-35B-A3B (~A4B) with Unsloth quants. On a (vant.ai) rented V100 16GB with the 2-bit UD-Q2_K_XL quant it generates at ~57-60 to

4 Oct 2026

Syntheses

Synthesis: Arxiv-Cs-Ai

synthesisarxiv-cs-aiauto-generated

Auto-generated synthesis of 29846 entries about arxiv-cs-ai

4 Oct 2026

A framework for auditing grounding claims

arXiv:2512.06205v3 Announce Type: replace Abstract: The symbol grounding problem asks how a token such as cat can be about cats. We propose a framework for auditing grounding claims against a declared

agentsarxiv-cs-ai

Adding memory to search instead of sampling in reward maximization tasks [R]

model-releasesr-machinelearning

I am one of the authors of FLEET - an algorithm that enhances Best-of-N generation by attributing external rewards to particular tokens and then uses MCTS to adjust logits during the next run. I find

ALER: Adaptive Learnable Experience Rewriting for Reinforcement Learning

arXiv:2610.00592v1 Announce Type: cross Abstract: In partially observable reinforcement learning (RL), a later observation can make stored information obsolete or change what it implies for the next d

Anti-Persona: Disrupting Unauthorized Identity Binding and Recognition in Personalized Vision--Language Models

arXiv:2610.01944v1 Announce Type: new Abstract: Few-shot personalization enables large vision--language models (LVLMs) to learn user-specific visual concepts for applications such as personalized retr

safetyarxiv-cs-cv

CHILLGuard: Towards Fine-Grained Chinese LLM Safety Guardrail with Scalable Data Construction and Model-aware Preference Alignment

arXiv:2606.15396v2 Announce Type: replace-cross Abstract: Malicious content generated from large language models (LLMs) could pose severe safety risks and ethical concerns. While existing LLM safety g

CLoSeR: Closing the Loop for Long-Context Streaming Reconstruction

arXiv:2610.01927v1 Announce Type: new Abstract: Feedforward foundation models have recently shown remarkable 3D reconstruction capabilities. However, existing models exhibit large tracking drift in lo

researcharxiv-cs-cv

Constant-Memory Recall: Learned Associations in a Fixed Matrix State

arXiv:2610.00232v1 Announce Type: new Abstract: Fixed-size recurrent memory limits storage growth during inference, but successful recall depends on the task and training. We study a small DeltaNet va

Cybernetic and Epistemic: A Missing Vocabulary for Trustworthy Agentic Delegation

arXiv:2610.00961v1 Announce Type: new Abstract: As code generation is increasingly delegated to AI systems, the bottleneck is shifting from writing code to supervising the systems that write it --- a

agentsarxiv-cs-ai

Distilling Directional Verification

arXiv:2610.00997v1 Announce Type: cross Abstract: Knowledge distillation aims to transfer the factual knowledge of large language models to smaller models for efficient deployment. Yet a teacher may r

Dyna-DINO: Efficient ViT Distillation Via Adaptive Representation Anchoring

arXiv:2606.19483v2 Announce Type: replace Abstract: Vision Foundation Models (VFMs) with Vision Transformer (ViT) backbones, such as DINOv2, have become essential for downstream tasks like object reco

researcharxiv-cs-cv

Local Ai

Enterprise Representation Simplification (ERS): Reducing Representational Complexity for Enterprise AI

arXiv:2610.00791v1 Announce Type: new Abstract: Enterprise information is represented through artifacts shaped by applications, projects, technologies, organizational boundaries, and local requirement

local-aiarxiv-cs-ai

External Observers May See More Clearly: Cross-Model Span-Level Hallucination Detection in Large Language Models via Hidden State Probing

arXiv:2610.02066v1 Announce Type: new Abstract: As Large Language Models (LLMs) increasingly serve as foundational reasoning engines, their tendency to hallucinate remains a critical vulnerability. Wh

FFBL-Coop: Association-Decoupled Cooperative 3D Multi-Object Tracking

arXiv:2610.01750v1 Announce Type: new Abstract: Cooperative 3D tracking must integrate complementary observations across agents and time while maintaining consistent identities. When evidence integrat

Fine-tune a search agent with multi-turn RL on Amazon SageMaker AI

Fine-tuning teaches a small search agent your tools and environment, giving it the reliability of a frontier model at lower latency and cost. In this post, we fine-tune an LLM-powered search agent wit

agentsaws-ml-blog

GPEC: Efficient Pre-LLM Gaussian Process Embedding Correction for Cardiac Video Caption Generation

arXiv:2610.00196v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have shown strong potential for video understanding and caption generation, but their performance may decline i

I benchmarked Qwen 3.8 27B on my M4 Max — here's what I learned about local models, context, and coding performance

I've been experimenting with running larger models locally with Ollama, mainly because I'm interested in using them for agentic coding. I kept seeing benchmarks expressed as something like: 40 tokens/

model-releasesr-ollama

InterviewSim: A Scalable Framework for Interview-Grounded Personality Simulation

arXiv:2602.20294v2 Announce Type: replace-cross Abstract: Simulating real personalities with large language models requires grounding generation in authentic personal data. Existing evaluation approac

Legal text classification in Korean sexual offense cases: from traditional machine learning to large language models with XAI insights

arXiv:2610.00087v1 Announce Type: cross Abstract: The advancement of natural language processing (NLP) has expanded AI-based text classification in the legal domain. However, accurately classifying le

LensBridge: Frequency-Guided Compound Degradation Adaptation for Lens Aberration Correction and Veiling Glare Removal

arXiv:2610.00318v1 Announce Type: cross Abstract: Simplified optical systems often exhibit residual lens aberrations and Veiling Glare (VG), resulting in spatially varying blur and contrast reduction.

researcharxiv-cs-cv

Mean field games as a tool for AI safety: a worked example from the July 2026 Hugging Face incident

arXiv:2610.00902v1 Announce Type: cross Abstract: One way to make AI systems safe is to shape what the system is: its objective and dispositions. We take a complementary route: treat the agents' chara

MemFit: Efficient Long-Term Agentic Memory

arXiv:2610.00872v1 Announce Type: new Abstract: Long-term memory systems for large language models (LLMs) have gained popularity for extending reasoning capabilities across applications. Current memor

agentsarxiv-cs-ai

MosaiChunk: Compositing Spatio-Temporal Memory for Autoregressive Video Generation

arXiv:2610.02153v1 Announce Type: new Abstract: Long-horizon autoregressive video generation is limited by a finite context window. When an object or scene falls out of context, its fine-grained visua

Not All Is Lost: Repairing Lossy User Preference States of Personalization Encoders

arXiv:2610.01270v1 Announce Type: new Abstract: Personalization encoders compress evolving interaction histories into preference states used to rank items or condition text generation. A task head ope

researcharxiv-cs-lg

One pool, many targets: a conservation layer and what archival data can identify

arXiv:2610.00445v1 Announce Type: new Abstract: Pairwise guide--transcript scores do not enforce conservation of a finite guide-loaded RISC pool when they are interpreted independently as occupancies.

RPTune: Learned Context Curation for LLM Catalog Search

arXiv:2610.00964v1 Announce Type: cross Abstract: For small merchant businesses (SMBs) whose catalogs fit within a long-context LLM, full-catalog prompting offers a compelling alternative to multi-sta

Scientific Agents: Evaluating Profession-Specific System Prompts on Scientific Tasks

arXiv:2610.00084v1 Announce Type: new Abstract: Detailed profession-specific system prompts raise token use and estimated cost per response without a consistent accuracy gain. We evaluate Scientific A

Skeleton-and-Strategy Prompting: Training-Free Negation Understanding for Vision-Language Models

arXiv:2610.01180v1 Announce Type: new Abstract: Despite the strong performance of Vision-Language Models (VLMs) on a wide range of visual question answering (VQA) tasks, these models consistently stru

Local Ai

SPHERE: Adaptive VR Indoor Scene Generation via LLM-Enhanced Spatial Preference Learning and Human-in-the-Loop RL

arXiv:2610.02023v1 Announce Type: new Abstract: While Large Language Models (LLMs) advance 3D indoor scene synthesis, current pipelines fail to retain user-specific preferences across sessions, making

local-aiarxiv-cs-ai

System Attribution in LLM Brand Recommendations: Single Responses Identify the System, Aggregated Brand Profiles Do Not Transfer

arXiv:2610.00253v1 Announce Type: cross Abstract: Audits of AI visibility summarise the brand recommendations of deployed language models into per-system profiles. We test whether such a profile descr

The Delegation Danger Band: Why Mid-Capability Sub-Agents Over-Trust Inherited Stale State

arXiv:2610.00041v1 Announce Type: cross Abstract: Agent frameworks increasingly delegate work by forking sub-agents; a common default makes the child inherit the parent's full working context. We meas

Two 96 GB Ascend cards crun Qwen3.8-flash-next hardware notes, vLLM work, benchmarks, and what is next

model-releasesr-localllama

I have been building a somewhat unusual local inference machine around two Huawei Atlas 300I Duo cards. They are relatively inexpensive, passive, dual-accelerator PCIe cards with 96 GB of device memor

VIEScore2: Unified Image Evaluation with Spatially Grounded Explanations

arXiv:2610.00994v1 Announce Type: new Abstract: Existing synthetic image evaluators typically provide only a scalar quality score and do not identify the image regions that support it. We introduce VI

VISTA: A Visual Harness for Reasoning in an Interactive World

arXiv:2610.02200v1 Announce Type: new Abstract: We show that multimodal models possess strong reasoning abilities and that an appropriate harness can unlock their potential to solve tasks across diver

What Does a Skill Actually Do? Estimands and Evaluation Validity for Tool and Skill Use in LLM Agents: A Critical Review

arXiv:2609.33153v2 Announce Type: replace-cross Abstract: Reported improvements from tools and reusable skills in large language model agents refer to different comparisons. This critical narrative re

When More Data Is Not Enough: The Context-Sufficiency Frontier in Generative AI Personalization

arXiv:2610.00654v1 Announce Type: new Abstract: Personalization has long relied on customer data to infer what an individual is likely to value. We call this customer evidence: the customer's historic