Search: “models”

DeepFlow: Two Convergent Differentiable Approaches for Pairwise Conditional Random Fields

arXiv:2610.16546v1 Announce Type: new Abstract: Basic probabilistic models form the basis of modern deep learning largely by defining the final activation layer of a network and allowing the networks

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DeKoR: Decomposed Knowledge Routing for Efficient GNN--LLM Fusion

DGX agent

arXiv:2610.14710v1 Announce Type: new Abstract: Large Language Models (LLMs) have shown strong potential for text-attributed graph learning by leveraging rich pre-trained knowledge and language unders

local-aiarxiv-cs-lg

14 Oct 2026

DPOison-VL: Clean Preference Poisoning in Vision-Language Direct Preference Optimization

arXiv:2610.18609v1 Announce Type: cross Abstract: Direct Preference Optimization (DPO) has become a widely adopted approach for aligning vision-language models (VLMs), yet the security of multimodal p

safetyarxiv-cs-cv

DuplexBridge: Coordinating Understanding and Generation for Unified Image Editing

arXiv:2610.18819v1 Announce Type: new Abstract: Complex image editing requires faithful task interpretation and accurate execution, but a failed edit does not reveal which responsibility failed. We pr

Dynamic Interpolation for Training-Time Prediction Reshaping in Low-Light Image Enhancement

arXiv:2610.18018v1 Announce Type: new Abstract: Low-light image enhancement (LLIE) is an important image/video processing problem for reliable visual systems under poor illumination, yet it remains ch

ECLIPTICA -- A Framework for Switchable LLM Alignment via CITA - Contrastive Instruction-Tuned Alignment

arXiv:2601.06157v2 Announce Type: replace Abstract: Alignment in large language models (LLMs) is still largely static: after training, the policy is frozen. DPO, GRPO methods typically imprint one beh

EgoAlign: Situated 3D Understanding without Situation Descriptions

arXiv:2610.16176v1 Announce Type: new Abstract: Situated 3D Question Answering requires models to interpret a 3D scene relative to an agent's pose. Existing methods commonly convey that pose through a

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EventLens: Event-Structure Reinforcement Learning for Video Understanding

arXiv:2610.16172v1 Announce Type: new Abstract: Recent multimodal large language models (MLLMs) achieve strong results on video question answering, yet often fail to recover the temporal structure tha

safetyarxiv-cs-cv

Exact Phase-Type Point Processes for Certified Measurement and Prediction of Event Streams

arXiv:2610.14948v1 Announce Type: new Abstract: Temporal point processes are used both to measure market mechanisms, for example how fast prices absorb news and how much of the reaction is the market

Fisher information and the geometry of memorization in neural networks

arXiv:2610.16161v1 Announce Type: new Abstract: A fruitful approach towards understanding generalization in machine learning involves characterizing how models separate generalizable structure from id

Local Ai

FlashVSR+: Efficient Video Super-Resolution via Hybrid Attention and Reward-Yield Distribution Matching

arXiv:2610.17552v1 Announce Type: new Abstract: Recent video super-resolution (VSR) models achieve efficient one-step generation through diffusion distillation. However, high-resolution inference rema

local-aiarxiv-cs-cv

FreeCHROMA: Generative Chroma Keying from Unaligned Background References

arXiv:2610.14445v1 Announce Type: new Abstract: In film production, chroma keying composites an approved foreground performance into a new environment, but conventional workflows require coordinated b

Agents

From Acoustic Sensing to Physical Actions: A Comprehensive Survey of Security Risks in Edge-Deployed Audio LLM-Based Agents

arXiv:2610.15390v1 Announce Type: cross Abstract: Audio Large Language Models (Audio LLMs) are embedded in agents for smart homes, vehicles, wearables, and embodied systems, where acoustic inputs can

agentsarxiv-cs-cv

From Suboptimal to Optimal: One-Step Latent Flow Policies for Offline Reinforcement Learning

arXiv:2610.16984v1 Announce Type: new Abstract: Offline reinforcement learning aims to learn effective policies from fixed datasets without environment interaction. Although generative models such as

Geometric Calibration of Supervised Fine-Tuning

arXiv:2610.18634v1 Announce Type: new Abstract: Recent studies investigate the weight geometry through its spectral structure, which characterizes how the weights transform different input directions.

Grables: When Graph Structure Helps Tabular Prediction

arXiv:2602.03945v2 Announce Type: replace Abstract: Simple row-wise predictors that score each row independently remain competitive with far more expressive architectures on tabular benchmarks, which

Harness^2: Recursive Agent Harnessing for an Open World

arXiv:2610.14224v1 Announce Type: cross Abstract: LLM agents' capabilities depend not only on their underlying models but also on their execution harnesses. Recursive self-improvement (RSI) offers a p

Hierarchical Curriculum Learning for Surgical Video Generation: Focusing on Bilateral Axillo-Breast Approach Robotic Thyroidectomy Datasets

arXiv:2610.17684v1 Announce Type: new Abstract: High-fidelity surgical video generation is increasingly important for surgical education, simulation, and downstream AI training, as conventional simula

Holistic Scaling Laws for Optimal Mixture-of-Experts Architecture Optimization

arXiv:2603.21862v2 Announce Type: replace Abstract: Scaling laws govern macroscopic resource allocation for LLMs, yet precise architectural configurations for Mixture-of-Experts (MoE) models remain gu

HyBRA-EEG: Hyperbolic Global-Local Brain-Region Aggregation for Self-Supervised EEG Representation Learning

arXiv:2610.16589v1 Announce Type: new Abstract: Electroencephalography (EEG) representation learning is challenged by noisy signals, inter-subject variability, and heterogeneous electrode montages in

HYDRA: Representation Harmonized Tokenization for Multimodal Generation and Understanding

arXiv:2603.15228v3 Announce Type: replace Abstract: Unified Multimodal Models struggle to bridge the fundamental gap between the abstract representations needed for visual understanding and the detail

In-context classification: Calibration and noise in linear attention with softmax readout

arXiv:2610.17061v1 Announce Type: cross Abstract: We study in-context binary classification in a simple attention model trained to minimise a logistic loss. Each prompt has a latent logistic teacher,

Look Back to Move Forward: Learning to Revisit for Agentic Compositional Image Generation

arXiv:2610.16224v1 Announce Type: new Abstract: Text-to-image models have made remarkable progress in visual fidelity and instruction following, yet complex compositional generation remains challengin

Mixed Precision Scaling Laws for Quantized Pretraining

arXiv:2610.17637v1 Announce Type: new Abstract: Since scaling laws were introduced for modern LLMs (Kaplan et al., 2020), several works have extended them to account for quantization, tracking the eme

MultiAlloc: Budget-Aware Precision Allocation for KV Cache Quantization

arXiv:2610.18642v1 Announce Type: new Abstract: The Key-Value (KV) cache constitutes a dominant source of memory consumption during large language model (LLM) inference, particularly in long-context a

On the Statistical Mechanics of Attention

arXiv:2610.17347v1 Announce Type: new Abstract: We revisit the statistical mechanics of Transformer attention via modern Hopfield models. We present a free-energy formalism to characterize parameter r

Online LLM Personalization from Pairwise Preferences Across Users

arXiv:2610.17203v1 Announce Type: new Abstract: Large language models can answer the same question in different styles, but users' preferred styles are hidden and vary across users and questions. We s

Perception Beyond the Visible: Incompleteness-Aware Scene Understanding for Robot Autonomy

arXiv:2610.18670v1 Announce Type: cross Abstract: Reliable robot autonomy requires reasoning about the world from partial and noisy observations. However, many perception systems model only visible sc

Q-Save: Towards Scoring and Attribution for Generated Video Evaluation

arXiv:2511.18825v3 Announce Type: replace Abstract: Evaluating AI-generated video (AIGV) quality hinges on three crucial dimensions: visual quality, dynamic quality, and text-video alignment. While nu

Resample the Answer: Where to Spend Test-Time Compute in Recursive Reasoning

arXiv:2610.16427v1 Announce Type: new Abstract: Recursive reasoning models such as the Tiny Recursive Model (TRM) iteratively refine two latent states: an answer state y, decoded into the output, and

Retrievable Gradients: Continual Post-Training Without Cumulative Weight Drift

arXiv:2606.15734v2 Announce Type: replace-cross Abstract: Continual post-training enables models to absorb emerging knowledge after deployment, but repeatedly updating shared parameters can accumulate

Sampling from Neural Operator Priors to Solve Partial Differential Equations

arXiv:2610.16396v1 Announce Type: new Abstract: Guided sampling with sparse observations and PDE residuals has recently been combined with neural operator priors to solve partial differential equation

Sanzaru: Benchmarking Protocol Formation under Epistemic & Communication Asymmetry

arXiv:2610.15163v1 Announce Type: cross Abstract: A language model's ability to solve a task individually does not ensure that a team can solve it under unequal access to information and communication

SceneForesee: Learning Scene Evolution for Spatiotemporal Reasoning

arXiv:2610.14439v1 Announce Type: new Abstract: Large multimodal models (LMMs) need to understand how entities move and interact as a scene develops. This requires following the same entities over tim

scHFBiMamba: a bidirectional Mamba autoencoder for cardiac disease-state classification from single-nucleus RNA-seq data

arXiv:2610.15117v1 Announce Type: new Abstract: To evaluate whether a compact bidirectional state-space autoencoder improves joint classification of species, cell type, and disease state from single-n

Screening Collapse under Recursive Synthetic Data

arXiv:2610.15617v1 Announce Type: cross Abstract: Recursive synthetic-data training can degrade a fitted model, but its effects on the variable-screening stage that precedes model fitting are less und

Shaping Latent Mixture-of-Experts: From Expert Geometry to Layerwise Capacity Allocation

arXiv:2610.14644v1 Announce Type: cross Abstract: Fine-grained mixture-of-experts (MoE) models commonly increase expert count by narrowing each expert, treating expert shape as a by-product of routing

SidewalkBench: Benchmarking Visual Navigation on Urban Sidewalks

model-releasesarxiv-cs-ro

arXiv:2606.16953v2 Announce Type: replace Abstract: Urban sidewalk navigation presents significant challenges due to complex structural layouts, dynamic pedestrian behaviors, and long distances. While

Spatiotemporal Recognition of Abdominal Wall Layers During Optical Trocar Entry

arXiv:2610.14512v1 Announce Type: new Abstract: Objective. To evaluate whether spatial context and temporal modeling improve tissue recognition during optical trocar entry, where multiple abdominal-wa

Splicing Hints into Reasoning Traces: Learning from Demonstrator Failures

arXiv:2610.13996v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards improves mathematical reasoning when a final answer can be checked, but a correct answer does not certify

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SurgiCLAW: An Interpretable Atomic-Skill Agent for Precision Robotic Micromanipulation

arXiv:2610.15766v1 Announce Type: new Abstract: Recent multimodal large language models (MLLMs) and vision--language--action models have provided robots with new capabilities for long-horizon task und

safetyarxiv-cs-ro

TempoKV: Predictive KV Cache Quantization for Autoregressive Video Generation

arXiv:2610.15324v1 Announce Type: new Abstract: Autoregressive (AR) video diffusion models enable streaming generation of long videos but suffer from significant memory overhead due to the rapidly gro

Agents

Terrarium: Growing Personal Assistants via Scalable Synthesis of App Worlds

arXiv:2610.14860v1 Announce Type: new Abstract: Advances in large language models (LLMs) have brought the vision of a general-purpose personal assistant closer to reality. Such assistants could provid

agentsarxiv-cs-lg

TIDE: Trajectory-Informed Skill Distillation for Efficient Text-to-Video Prompt Optimization

arXiv:2610.15098v1 Announce Type: new Abstract: Text-to-video (T2V) models are highly sensitive to prompt specificity, while iterative prompt optimization typically improves generation quality only th

Towards Green AI: Evaluating Energy Efficiency for Quantized LLM Inference

arXiv:2610.17531v1 Announce Type: new Abstract: While it is known that quantization of large language models (LLMs) reduces memory usage via lower-bit weights, studies quantifying the resulting impact

Trust-Calibrated VLA Policy Post-Training with Evolving Imagination

arXiv:2610.16517v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models are often pretrained via imitation learning, making reinforcement learning (RL) post-training important when deplo

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TurnCost: Turns, Not Observations: Cache-Aware Loop Engineering for Coding Agents

arXiv:2610.15093v1 Announce Type: cross Abstract: AI coding agents operate in iterative loops: they modify code, run tools, inspect the results, and decide what to do next. We call the design of this

UnSAMv2: Self-Supervised Learning Enables Segment Anything at Any Granularity

arXiv:2511.13714v2 Announce Type: replace Abstract: The Segment Anything Model (SAM) family has become a widely adopted vision foundation model, but its ability to control segmentation granularity rem