Search: “inference”

Landau theory of quenched criticality in linear in-context learning

arXiv:2608.28059v1 Announce Type: cross Abstract: In-context learning (ICL) allows a pretrained model to infer a new task from examples supplied in its prompt without updating its parameters. In linea

31 Aug 2026

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Physics-informed learning for the inverse problem in resonant ultrasound spectroscopy

DGX agent

arXiv:2608.27590v1 Announce Type: cross Abstract: Inferring elastic constants from resonant ultrasound spectra is a nonlinear and typically overdetermined inverse problem based on finite spectral data

model-releasesarxiv-cs-lg

31 Aug 2026

Applications

Pruning Laws for Large Language Models

arXiv:2504.04342v2 Announce Type: replace Abstract: Scaling up model parameters and training data consistently improves the performance of large language models (LLMs), but at the cost of rapidly grow

applicationsarxiv-cs-cl

31 Aug 2026

Sliding-window beats linear attention

arXiv:2608.28444v1 Announce Type: new Abstract: Due to the nature of quadratic attention, Large Language Models (LLMs) consume a lot of memory and energy. Every new token costs more than the previous

31 Aug 2026

Assessing mentalization in humans and large language models

model-releasesarxiv-cs-ai

arXiv:2608.26291v1 Announce Type: new Abstract: Mentalization - the ability to infer others' beliefs and intentions to guide one's own choices - is a key cognitive function underlying human social int

Decoupled I/O-Dominant Pipelines for Large-Scale Whole-Slide Image Embedding Extraction

arXiv:2608.27278v1 Announce Type: cross Abstract: Whole-slide images (WSIs) are central to computational pathology but are prohibitively large, making patch-based processing the practical unit for fou

researcharxiv-cs-cv

Don't Overthink, Don't Underthink: Toward Adaptive Reasoning in Agentic AI

model-releasesarxiv-cs-ai

arXiv:2608.26442v1 Announce Type: new Abstract: Recent advances in Large Language Models (LLMs) have shown that increased inference-time reasoning can improve performance on complex tasks. However, ma

EditaLive! Unified Character Video Editing for Live Streaming

arXiv:2608.27123v1 Announce Type: new Abstract: Conventional video editing primarily focuses on scene-level content, whereas live streaming places greater emphasis on the human subject. However, direc

researcharxiv-cs-cv

Applications

Lost in Compression: A Controlled Cross-Lingual Audit of Extractive Prompt Compressors

arXiv:2608.26175v1 Announce Type: cross Abstract: Extractive prompt compression promises to cut LLM inference costs by removing low-information tokens, and learned compressors such as LLMLingua-2 repo

applicationsarxiv-cs-ai

MM-Spectrum: Multimodal Multi-spectral Molecular Structural Elucidation with a Stable MoE Framework

arXiv:2608.27286v1 Announce Type: new Abstract: Inferring molecular structures from multimodal spectroscopic measurements requires integrating complementary yet highly heterogeneous signals. However,

researcharxiv-cs-lg

Over-The-Air Extreme Learning Machines with Nonlinear Stacked Intelligent Metasurfaces

arXiv:2608.27137v1 Announce Type: cross Abstract: The recently envisioned goal-oriented communications paradigm requires machine learning inference to be performed directly on wirelessly transferred d

researcharxiv-cs-lg

PredVLA: A Sub-Million-Parameter Predictive-Coding Policy for Robot Manipulation

model-releasesarxiv-cs-ro

arXiv:2608.26673v1 Announce Type: new Abstract: Large pretrained vision-language-action models dominate modern robot-manipulation benchmarks, but it remains unclear how much model scale is necessary f

Recipes for Steering and Scaling LLMs via Sampling

arXiv:2608.26120v1 Announce Type: new Abstract: Large Language Models (LLMs) are probabilistic models, typically defined by an autoregressive factorization. While recent work has begun to study richer

Redwood: A Frontier AI Accelerator Designed, Verified, and Deployed from Scratch in 2 Weeks by AI

model-releasesarxiv-cs-ai

arXiv:2608.26418v1 Announce Type: cross Abstract: Modern AI workloads and the hardware that runs them evolve on different timescales: architectural definition precedes volume silicon by years, while t

Agents

RTNav: Towards Real-Time Zero-Shot Object Navigation

arXiv:2608.26496v1 Announce Type: cross Abstract: Navigation in unknown environments to find unforeseen objects has become increasingly feasible with capable vision and language foundation models. How

agentsarxiv-cs-ai

Agentic Autoresearch for Cell-Edge Power Control: Radically Redefining the Researcher's Role

arXiv:2608.26093v1 Announce Type: new Abstract: Designing machine learning algorithms for wireless resource management is labour-intensive: the architecture, the loss function and the training recipe

AlgoTrace: Algorithmic Primitives and Compositional Geometry of Reasoning in Language Models

arXiv:2510.15987v3 Announce Type: replace Abstract: How do inference time and latent computations enable large language models (LLMs) to solve multi-step reasoning problems? We introduce AlgoTrace, a

Agents

AsymSpec: Context-Asymmetric Speculative Decoding for Agentic LLMs

arXiv:2608.26004v1 Announce Type: cross Abstract: Agentic LLM pipelines face escalating inference costs as context accumulates across retrieval, tool use, and multi-turn interactions. To control laten

agentsarxiv-cs-cl

CVE-SAI: Counterfactual Visual Evidence-Guided Selective Attribute Indexing for Risk-Controlled E-commerce Search

arXiv:2608.25023v1 Announce Type: cross Abstract: Multimodal product models can complete missing e-commerce attributes, yet current methods still optimize attribute-answer accuracy without verifying v

local-aiarxiv-cs-cv

Does Fine-Tuning Undo Activation Steering? Behavioural Recovery Without Weight-Edit Reversal

arXiv:2608.24988v1 Announce Type: new Abstract: Activation steering can be embedded directly into a language model's weights, shaping behaviour without inference-time intervention and offering a way t

safetyarxiv-cs-cl

GRIP: Granular Reward-Guided Parameter Interpolation for Efficient Reasoning

model-releasesarxiv-cs-cl

arXiv:2608.25583v1 Announce Type: new Abstract: Reasoning-oriented large language models often achieve strong problem-solving performance by generating long chains of thought, but this behavior substa

Learning to Accelerate Vision-Language-Action Models through Adaptive Visual Token Caching

arXiv:2602.00686v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models have demonstrated remarkable generalization capabilities in robotic manipulation tasks, yet their substantial co

safetyarxiv-cs-ro

Multi-View Trust Evaluation for Collaborator Selection via Evidential Deep Learning

arXiv:2608.25235v1 Announce Type: cross Abstract: Selection of trustworthy collaborators in distributed systems is critical for efficient task completion, necessitating the inference of trustworthines

researcharxiv-cs-lg

Tutorials

TTSR: Test-Time Self-Evolving via Reflection

arXiv:2603.03297v2 Announce Type: replace Abstract: Test-time training (TTT) adapts large language models (LLMs) during inference using only unlabeled test inputs. Existing methods, however, face two

tutorialsarxiv-cs-cl

VDAR-Router: Adaptive LLMs Routing via Verbalized Query Difficulty Analysis Retrieval

arXiv:2607.18098v2 Announce Type: replace Abstract: Large language models are increasingly used in practical systems, making efficient model selection important for reducing deployment cost. LLM routi

When Personality Meets Quantization: A Layer-wise MBTI Analysis of Quantized LLMs

arXiv:2608.25977v1 Announce Type: new Abstract: Personality is increasingly important in large language models (LLMs), as it shapes users' trust, engagement, and emotional experiences. While the Myers

ASemConsist: Adaptive Semantic Feature Control for Training-Free Identity-Consistent Generation

arXiv:2512.23245v3 Announce Type: replace Abstract: Recent text-to-image diffusion models have significantly improved visual quality and text alignment. However, generating a sequence of images while

safetyarxiv-cs-cv

Can Large Language Models Still Explain Themselves? Investigating the Impact of Quantization on Self-Explanations

arXiv:2601.00282v2 Announce Type: replace-cross Abstract: Quantization is widely used to accelerate inference and streamline the deployment of large language models (LLMs), yet its effects on self-exp

researcharxiv-cs-ai

GlanceWAM: Sparse Test-Time Imagination for World-Action Models

model-releasesarxiv-cs-cv

arXiv:2608.23927v1 Announce Type: new Abstract: Video generative models provide rich physical priors for robot learning, yet existing world-action models (WAMs) face a fundamental trade-off: synchrono

Maia 200: A Software Defined Dataflow System for Large-scale AI Acceleration

arXiv:2608.24664v1 Announce Type: cross Abstract: We introduce Maia 200, an advanced AI accelerator delivering high performance-10 145 Tflop/s FP4 and 5072 Tflop/s FP8 within a 750W TDP and 7 TB/s HBM

local-aiarxiv-cs-ai

PuzzleKV: Page-Wise Low-Rank Decomposition for KV Cache Compression

arXiv:2608.23843v1 Announce Type: new Abstract: Long-context inference in large language models (LLMs) is increasingly limited by the memory required for the key-value (KV) cache. KV cache compression

Hardware

RT-NeuS: Towards Real-Time Neuro-Symbolic Video Understanding via Adaptive Temporal Verification

arXiv:2602.23553v2 Announce Type: replace Abstract: Long-form video question answering (LVQA) requires answering natural-language queries about videos spanning minutes to hours, demanding temporal rea

hardwarearxiv-cs-cv

Agents

VizAnchor: Decoding Manipulation Intent from Tampering Visualizations via Dual-Anchor Reasoning

arXiv:2608.24535v1 Announce Type: new Abstract: Data visualizations are widely used for communicating information, but they are also vulnerable to intentional manipulations that induce misleading inte

agentsarxiv-cs-cv

Accelerating Diffusion Language Models via Structured Suffix Modeling

arXiv:2608.23167v1 Announce Type: new Abstract: Diffusion Language Models (DLMs) exhibit strong parallel decoding capabilities by denoising multiple tokens in a single generation step. However, this p

local-aiarxiv-cs-cl

AI Learning and Conceptual Transfer in the Game of Hidden Rules

arXiv:2608.21372v1 Announce Type: new Abstract: This report summarizes the work conducted on the Game of Hidden Rules (GOHR), focusing on reinforcement learning agents trained to infer hidden rules fr

researcharxiv-cs-ai

Breaking the Assumptions: Auditing Input-Side Jailbreak Defenses Against Semantic Attacks

arXiv:2608.21895v1 Announce Type: cross Abstract: Locally deployed Large Language Models (LLMs) via inference engines such as Ollama run without the moderation and abuse detection present in API-serve

local-aiarxiv-cs-ai

Calibrate What You SHIP: Post-Selection Risk Control for Verifier-Guided Text-to-Image Generation

model-releasesarxiv-cs-cv

arXiv:2608.21748v1 Announce Type: new Abstract: Verifier-guided text-to-image systems increasingly use test-time search to select, refine, or stop among multiple candidates, yet release thresholds are

CD-LoRA: Consistency-Driven Low-Rank Adaptation for Multi-Task Fine-Tuning

arXiv:2608.21909v1 Announce Type: new Abstract: While Multi-Task Learning (MTL) is essential for adapting Large Language Models (LLMs) to diverse domains, prevailing LoRA-based methods rely on complex

safetyarxiv-cs-lg

E2S-Pruner: Progressive Two-Stage Evidence Fusion for Visual Token Pruning in Vision-Language Models

arXiv:2608.23253v1 Announce Type: cross Abstract: Vision-language models typically encode an image into hundreds of visual tokens, incurring substantial inference latency and GPU memory overhead. Exis

local-aiarxiv-cs-ai

FIRM-Video: Check Before You Score for Reliable Text-to-Video Reward Modeling

arXiv:2608.21839v1 Announce Type: new Abstract: Reliable reward models are essential for text-to-video evaluation and alignment. However, the trade-off between evaluation accuracy and inference effici

safetyarxiv-cs-cv

HiViS: Hiding Visual Tokens from the Drafter for Speculative Decoding in Vision-Language Models

arXiv:2509.23928v3 Announce Type: replace-cross Abstract: Speculative decoding has proven effective for accelerating inference in Large Language Models (LLMs), yet its extension to Vision-Language Mod

safetyarxiv-cs-ai

On the Capability Separation Between World-Model Policy Learning and Imitated World-Action Models

arXiv:2608.22197v1 Announce Type: new Abstract: World-action models predict a future outcome and then infer an associated action. Although this factorization can improve representation learning and da

safetyarxiv-cs-lg

Retrieval-Augmented Visual Prompting: Guiding Foundation Models in Two-Photon Imaging

model-releasesarxiv-cs-cv

arXiv:2608.21970v1 Announce Type: new Abstract: Two-photon calcium imaging presents a challenging setting for foundation models: image appearance varies substantially across recordings and experimenta

Robust Bimanual Vision-Language-Action Models via Embarrassingly Simple Modality Masking

arXiv:2608.22419v1 Announce Type: cross Abstract: Query-based Vision-Language-Action (VLA) models offer low-latency inference that is attractive for bimanual robotic manipulation, but we observe that

safetyarxiv-cs-cv

Hardware

RS^3-Prune: Read-Sparse, Store-Sparse Token Pruning for Video Object Segmentation

arXiv:2608.22526v1 Announce Type: new Abstract: We introduce RS^3-Prune, a training-free token-pruning recipe that instantiates as a small set of inference time hooks atop existing video object segmen

hardwarearxiv-cs-cv

SANE: State Anomaly Neutralization for Stable Extreme-Context Delta-Rule Models

arXiv:2608.22354v1 Announce Type: cross Abstract: Delta-Rule recurrent models maintain a fixed-size state, enabling O(1) inference memory but potentially becoming unstable under extreme-context extrap

researcharxiv-cs-ai

ST^2U: Stateful Test-Time Unlearning via Restricted Knowledge Boundary Control

model-releasesarxiv-cs-cl

arXiv:2608.23034v1 Announce Type: cross Abstract: Controlling restricted knowledge in large language models is essential for model alignment and safe deployment. Test-time unlearning avoids costly ret

STONIC: A Layered Measurement Contract for LLM Value Profiling

arXiv:2608.23411v1 Announce Type: new Abstract: LLM value studies often merge questionnaire ratings, pairwise choices, and values inferred from generated text into one profile. That merge assumes that