Search: “inference”

Using Fine-Tuned LLMs to Identify Indicators of Vulnerability in UK Police Incident Logs

arXiv:2607.18446v2 Announce Type: replace Abstract: Purpose: Understanding how much of routine policing involves vulnerable people could inform resourcing, training, and multi-agency response, yet adm

local-aiarxiv-cs-cl

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Model Releases

Values as Style: Disentangling Values from Semantics with One-Way Mixing for Low-Damage LLM Steering

DGX agent

arXiv:2609.39701v1 Announce Type: new Abstract: Value steering should change an LLM's normative priorities while preserving the scenario, facts, and task constraints underlying its answer. Conventiona

model-releasesarxiv-cs-ai

1 Oct 2026

VirusCascade: Hijacking Collaborative Reflection in LLM-Powered Recommender Agents

arXiv:2609.38270v1 Announce Type: cross Abstract: Advancing beyond traditional static scoring models, LLM-powered agentic recommender systems (LLM-ARS) instantiate users and items as autonomous agents

local-aiarxiv-cs-lg

WEIRDO: WEak resIdual Regularized DOob's h-transform diffusion alignment

arXiv:2609.39531v1 Announce Type: cross Abstract: We study the problem of estimating the guidance that steers the distribution learned by a diffusion generative model toward a tilted target q_0 propto

safetyarxiv-cs-lg

When a Kindergartener Solves Calculus: Measuring Capability Leakage in Role-Prompted Reasoning Models

arXiv:2609.39846v1 Announce Type: cross Abstract: We investigate the problem of role-capability leakage (RCL), in which a role-prompted reasoning model generates convincing in-role text while continui

When Reasoning Goes Astray: Attention Dynamics of Uncontrolled Reasoning

arXiv:2609.38817v1 Announce Type: new Abstract: Large reasoning models (LRMs) improve performance on complex tasks through extended reasoning, yet the same process can degenerate into redundant verifi

researcharxiv-cs-ai

Where Scientific Search Agents Fail: Decision-Checkpoint Auditing of Exposure and Inspection Attempts

arXiv:2609.38670v1 Announce Type: new Abstract: Final-answer accuracy does not reveal whether a scientific-search agent failed to encounter a target paper, attempt to inspect it, or return an accepted

Working Around the Compute Ceiling: Byte-Exact Memory in Galahad Makes LLM Reading a One-Time Cost LLM Reading a One-Time Cost

arXiv:2609.39358v1 Announce Type: cross Abstract: A transformer language model performs a bounded amount of computation per token, and recent work by Vishal Sikka, former CEO of Infosys, argues that t

A Mesoscopic View of Transformer Weights Through Row and Column Scale Fields

arXiv:2609.35852v1 Announce Type: new Abstract: Pooled statistics of Transformer weights obscure how magnitude is distributed across functional channels, while individual weights are too numerous to c

researcharxiv-cs-lg

A Polyphonic Conception of AI Understanding

arXiv:2609.36079v1 Announce Type: new Abstract: When a doctor, a judge, or an engineer must decide whether to trust an AI model's output, they cannot avoid asking what the model understands. Purely ma

local-aiarxiv-cs-ai

Act First, Reason Later: Accelerating On-Policy Distillation for Multi-Turn Agents via Reference-Conditioned Inverse Dynamics

model-releasesarxiv-cs-cl

arXiv:2609.36608v1 Announce Type: cross Abstract: On-policy distillation (OPD) trains multi-turn language agents with dense teacher supervision on student-generated responses. However, standard think-

Agents

ActiveSAM: Fast and Accurate Open-Vocabulary Semantic Segmentation with Frozen SAM 3

arXiv:2606.16996v2 Announce Type: replace-cross Abstract: Segment Anything Model 3 (SAM 3) provides a strong frozen backbone for concept-prompted segmentation, but applying it directly to open-vocabul

agentsarxiv-cs-ai

Adapting Context Compression for Long-Horizon Agents with Counterfactual Continuations

arXiv:2609.36526v1 Announce Type: cross Abstract: Long-horizon agents require context compression to manage growing interaction histories. Compression quality, however, is ultimately determined by dow

Adaptive Consistency Graph for Long-Horizon Agents

arXiv:2609.32754v2 Announce Type: replace Abstract: Large language model agents can often make reasonable local decisions on short tasks, yet their performance degrades when success requires long sequ

Agents

After the Fix: Transfer of Corrected Agent Experience

arXiv:2609.34603v2 Announce Type: replace Abstract: Does repairing an episode make its experience a better memory for the next task? We transfer the same failed source before and after accepted repair

agentsarxiv-cs-ai

Architecture Alignment With Sparse Priors in Tabular Foundation Models

model-releasesarxiv-cs-lg

arXiv:2609.36883v1 Announce Type: new Abstract: Tabular foundation models (TFMs) are increasingly popular because they deliver strong predictions on new datasets through in-context learning, without t

ARK: A Dual-Axis Multimodal Retrieval Benchmark along Reasoning and Knowledge

arXiv:2602.09839v2 Announce Type: replace Abstract: Existing multimodal retrieval benchmarks largely emphasize semantic matching on daily-life images and offer limited diagnostics of professional know

AS^2D: Accelerating On-Demand Audio Understanding on Mobile Devices

arXiv:2609.37617v1 Announce Type: cross Abstract: Speculative decoding accelerates autoregressive generation by using a smaller drafter to propose tokens for batched verification by a larger target. H

researcharxiv-cs-ai

Agents

Asymmetric Scout-Worker Reconnaissance for Route Validation in Unknown Environments

arXiv:2609.36537v1 Announce Type: new Abstract: This paper studies asymmetric scout-worker reconnaissance in unknown environments, where a small, agile autonomous scout explores routes for a larger wo

agentsarxiv-cs-ro

ATTUNER: Recomputation-Free KV Cache Reuse via Query-Side Adaptation

arXiv:2609.36722v1 Announce Type: new Abstract: Large language model (LLM) agents repeatedly load reusable content, such as skills, documents, and memory entries, into the current context. Re-encoding

local-aiarxiv-cs-cl

Authority Before Utility: Non-Compensatory Control for Persistent LLM Memory

arXiv:2609.37474v1 Announce Type: new Abstract: Persistent memory creates a control problem that retrieval relevance alone does not solve: a memory can remain highly useful after an update, deletion,

researcharxiv-cs-ai

BaLEEN: Biasing with Latent Encoded Entities for Context-Aware ASR

arXiv:2609.36913v1 Announce Type: cross Abstract: Transcribing domain-specific entities and rare proper nouns remains a major challenge in automatic speech recognition (ASR). In this paper, we propose

safetyarxiv-cs-ai

BarcodeMAE+: Rethinking Masked Pretraining and Global Representations for DNA Barcode Foundation Models

arXiv:2609.35877v1 Announce Type: cross Abstract: Many DNA foundation models are pretrained by masking parts of a sequence and asking the model to reconstruct them. Standard masked pretraining exposes

researcharxiv-cs-lg

Beyond Attention Imbalance: Mitigating Hallucinations via Spectral Surgery

arXiv:2609.37263v1 Announce Type: new Abstract: While Large Vision-Language Models (LVLMs) achieve remarkable success, hallucinations remain a significant barrier to their reliable deployment. Recent

researcharxiv-cs-cv

Applications

Beyond Discrimination: Calibrated Geoprior Fusion for Bioacoustic Monitoring

arXiv:2609.35863v1 Announce Type: cross Abstract: Modern bioacoustic foundation models like Perch and BirdNET can identify species with high discriminative accuracy, yet their confidence scores are of

applicationsarxiv-cs-lg

Beyond Interaction Capacity: Estimator Scaling with Recursive Models for CTR Prediction

arXiv:2609.37905v1 Announce Type: cross Abstract: Click-Through Rate prediction, a core task in recommendation and advertising systems, relies on modeling interactions among sparse categorical feature

Beyond Lip Sync: Reference-Grounded Oral Refinement for Audio-Driven Portrait Animation

arXiv:2609.38019v1 Announce Type: new Abstract: We present RGOR (Reference-Grounded Oral Refinement), an audio-driven lip-sync framework that renders the mouth of the specific person being dubbed rath

Hardware

BiFE: Search-Efficient Discovery of CPU-Only Branching Policies via LLM-based Bi-Fidelity Evolution

arXiv:2609.36735v1 Announce Type: new Abstract: In branch-and-bound (B&B) for mixed-integer linear programming (MILP), branching variable selection critically impacts efficiency. Existing neural branc

hardwarearxiv-cs-ai

Block Sparse Flash Attention

model-releasesarxiv-cs-cl

arXiv:2512.07011v2 Announce Type: replace-cross Abstract: Modern large language models increasingly require long contexts for reasoning and multi-document tasks, but attention's quadratic complexity c

Calibrate the Decisions That Change the Future: On-Policy Post-Training Quantization for Multimodal Large Language Models

arXiv:2609.36828v1 Announce Type: new Abstract: Post-training quantization (PTQ) lowers deployment cost for multimodal large language models, but calibration typically reconstructs fixed sequences wit

cktFormer: Transformer-Based Approach for Automated Analog Circuit Design

model-releasesarxiv-cs-lg

arXiv:2609.36752v1 Announce Type: new Abstract: Circuit design is a complex and iterative process that requires expertise in electronic engineering. It involves selecting components while meeting perf

CogWAM: Aligning Semantic Cognition with World Action Modeling via Event-Driven Interfaces

arXiv:2609.37721v1 Announce Type: cross Abstract: Robot policies increasingly incorporate semantic reasoning and future-world prediction, yet combining these capabilities does not guarantee that local

local-aiarxiv-cs-cv

Collision-Aware and Observation-Aligned Object-Centric Scene Reconstruction from Point Cloud

arXiv:2609.37260v1 Announce Type: new Abstract: Object-centric scene reconstruction requires completing partial object observations while preserving metric alignment and avoiding collisions with the s

local-aiarxiv-cs-cv

ComManip: Overfitting Manipulation Policies to Comfortable Regions

arXiv:2609.36928v1 Announce Type: new Abstract: Training robot manipulation policies relies on costly robot demonstrations, making large-scale data collection impractical. Meanwhile, to improve policy

local-aiarxiv-cs-ro

ContextRender: From Execution Dependencies to Agent Context

arXiv:2609.37743v1 Announce Type: new Abstract: LLM agents performing long-horizon tasks accumulate tool results that later steps may need. Passing the full history to every invocation is costly even

Cross-Entropy Guided Routing in Mixture-of-Experts Large Language Models

arXiv:2609.37751v1 Announce Type: new Abstract: Sparse mixture-of-experts (MoE) large language models scale model capacity by routing each token to a small subset of experts. Their routers are regular

DARE to Mitigate Hallucination: Dual-path Auto-Regressive-aware Editing

arXiv:2609.36440v1 Announce Type: new Abstract: Large vision-language models (LVLMs) have recently achieved remarkable progress across multimodal tasks, yet object hallucination remains a persistent c

researcharxiv-cs-cv

Data Agent Kit is now GA: Bring Google Data Cloud to any coding agent

model-releasesgoogle-cloud-ai

Today, Google Cloud Data Agent Kit is generally available. Data Agent Kit is a free set of Model Context Protocol (MCP) tools and agent skills that lets the coding agent you already use work directly

Data-Free Pruning of Self-Attention Layers in LLMs

arXiv:2512.20636v2 Announce Type: replace-cross Abstract: Many self-attention sublayers in large language models (LLMs) can be removed with little to no loss. We attribute this to the Attention Suppre

Decoding Affective Nuances: Enhancing MLLMs via Hierarchical Emotion Reasoning and Contrastive Discriminative Pruning

arXiv:2609.36782v1 Announce Type: new Abstract: While multimodal large language models (MLLMs) have demonstrated exceptional capabilities in objective understanding tasks, their performance in affecti

researcharxiv-cs-cv

Decompose Radicals, Then Reward: Fine-Grained Inspection for Accurate Chinese Text Rendering

arXiv:2609.37569v1 Announce Type: new Abstract: Rendering accurate Chinese text remains challenging for text-to-image models. Existing OCR-based reinforcement-learning rewards compare decoded transcri

Direct Experience World-Model Optimization: Learning the World Beyond Action Imitation

arXiv:2609.37398v1 Announce Type: new Abstract: World-Action Models (WAMs) couple action generation with predictions of how physical interactions unfold. However, current post-deployment learning para

safetyarxiv-cs-ai

Distill Locally, Schedule Globally: Flow Maps for Few-Step Text-to-Speech

arXiv:2609.36324v1 Announce Type: cross Abstract: Flow-matching text-to-speech (TTS) models achieve high synthesis quality but require many neural function evaluations (NFEs) to integrate their genera

local-aiarxiv-cs-lg

DMA^2: Pixel-space Distribution Matching with Adversarial and Anchor Losses

arXiv:2609.38156v1 Announce Type: new Abstract: Distribution matching distillation (DMD) provides a general framework for few-step diffusion generation, but its modern text-to-image instantiations hav

Does a prosody-trained representation help beyond trainable fusion? A parameter-matched study with frozen HuBERT

model-releasesarxiv-cs-cl

arXiv:2609.36754v1 Announce Type: cross Abstract: Explicit prosodic cues may help automatic speech recognition (ASR) of spontaneous speech, but auxiliary representations typically require additional t

Drag as Evidence: Motion-Grounded Latent Recomposition for Drag-Based Editing

arXiv:2609.36755v1 Announce Type: new Abstract: Modern image editors excel at semantic manipulation and visual synthesis, yet remain limited in precise spatial control, motivating the development of d

Dynamic Optimizations of LLM Ensembles with Two-Stage Reinforcement Learning Agents

arXiv:2502.04492v3 Announce Type: replace Abstract: The advancement of LLMs and their accessibility have triggered renewed interest in multi-agent reinforcement learning as robust and adaptive framewo

safetyarxiv-cs-cl

EASE: Behavior-Adaptive Skill Curation for Self-Evolving Agents

arXiv:2609.36746v1 Announce Type: new Abstract: Agent skills provide a lightweight mechanism for self-evolving agents to accumulate reusable procedural knowledge without updating model parameters. How