Search: “framework”

Learning Spatiotemporal Decision Priors for Efficient Path Planning under Partial Observability

arXiv:2607.22166v1 Announce Type: new Abstract: Path planning under partial observability remains challenging because an agent must make long-horizon navigation decisions from only locally bounded obs

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MA-DAR: Manifold-Aligned Dynamic Adaptive Routing for Continual Temporal Knowledge Graph Reasoning

DGX agent

arXiv:2607.21949v1 Announce Type: new Abstract: Continual temporal knowledge graph (TKG) reasoning aims to continuously incorporate newly emerging facts while preserving previously acquired knowledge.

safetyarxiv-cs-lg

27 Jul 2026

Nanbeige4.2-3B: Unlocking Agentic Capabilities in a Compact Mode

arXiv:2607.22083v1 Announce Type: cross Abstract: We present Nanbeige4.2-3B, a compact general agentic model with 3B non-embedding parameters. It delivers strong performance across code-agent, office-

safetyarxiv-cs-cl

Natural Invariant Measures for Chaotic Game Dynamics: Finding Order in Chaos

arXiv:2607.21805v1 Announce Type: cross Abstract: We study the long-term behavior of the Multiplicative Weights Update (MWU) algorithm in game settings where learning dynamics frequently fail to conve

Offline Vision-Language Navigation with Geometric Goal Localization for Outdoor Environments

model-releasesarxiv-cs-ro

arXiv:2607.22226v1 Announce Type: new Abstract: Foundation-model-based vision-language navigation (VLN) has advanced autonomous robot navigation by enabling robots to interpret natural-language instru

On Improving Faithfulness of Podcasts from Documents

arXiv:2607.21961v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to generate long-form conversational content such as podcasts from textual sources. While these syste

researcharxiv-cs-cl

Probing Latent Colombian Identity Inferences in Qwen2.5-7B with Natural Language Autoencoders

arXiv:2607.21774v1 Announce Type: new Abstract: Large language models may infer demographic attributes from subtle linguistic cues even when those attributes are not explicitly stated. This pilot stud

safetyarxiv-cs-cl

RED-PIM: Reducing Data Movement for Transformers using Processing-in-Memory

arXiv:2607.21731v1 Announce Type: new Abstract: Transformers are widely used across many domains, including natural language processing, computer vision, web search, and DNA sequence analysis. Given t

local-aiarxiv-cs-lg

Scaling Native Multimodal Pre-Training From Scratch

arXiv:2607.22043v1 Announce Type: new Abstract: Although large language models (LLMs) exhibit remarkable reasoning capabilities, their reliance on text-only pre-training restricts the perception of th

researcharxiv-cs-cl

Security Without Detection: Economic Denial as a Primitive for Edge and IoT Defense

arXiv:2512.23849v2 Announce Type: replace-cross Abstract: Sophisticated attackers can evade detection-based security by using encryption, stealth tactics, and low-rate attack patterns. This challenge

safetyarxiv-cs-lg

Self-Guided Process Reward Optimization with Redefined Step-wise Advantage for Process Reinforcement Learning

arXiv:2507.01551v3 Announce Type: replace-cross Abstract: Process Reinforcement Learning~(PRL) has demonstrated considerable potential in enhancing the reasoning capabilities of Large Language Models~

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Smart predict-then-robustly-optimize

arXiv:2607.21773v1 Announce Type: new Abstract: In this paper, we propose and study a robust variant of the smart predict-then-optimize approach that accounts for prediction shifts due to disturbance

Teaching LLMs to Self-Evolve: Cultivating Core Meta-Skills with Reinforcement Learning

arXiv:2607.21971v1 Announce Type: cross Abstract: Test-time scaling through iterative self-evolution with environment feedback, as demonstrated by AlphaEvolve, shows remarkable performance gains. We h

researcharxiv-cs-cl

Applications

Token-Operations-Oriented Inference Optimization Techniques for Large Models

arXiv:2606.20295v2 Announce Type: replace-cross Abstract: Large model inference optimization serves as a key foundation for supporting the scalable, low-cost, and highly stable operation of large mode

applicationsarxiv-cs-cl

Unbiased Open World Regularization for Fair Self-Supervised Learning

model-releasesarxiv-cs-lg

arXiv:2607.22149v1 Announce Type: new Abstract: Despite recent advances, self-supervised learning (SSL) models and Joint-Embedding Predictive Architectures (JEPAs) remain susceptible to learning spuri

Hardware

Unified Static-Dynamic Pruning for Efficient LLM Inference

arXiv:2607.21985v1 Announce Type: cross Abstract: The increasing deployment of large language models (LLMs) has magnified the computational and memory bottlenecks of autoregressive decoding, where low

hardwarearxiv-cs-lg

Variational Low-rank Tensor Decomposition for Multisubject Spatiotemporal Data Analysis

model-releasesarxiv-cs-lg

arXiv:2607.22262v1 Announce Type: cross Abstract: Modeling shared and subject-specific structure in multisubject spatiotemporal data remains challenging, particularly in neuroimaging, where both spati

Visual Relocalization from Sparse Views in Aliased and Low-Texture Environments via Novel View Synthesis

arXiv:2607.22147v1 Announce Type: new Abstract: Visual localization becomes extremely challenging in planetary-like terrains characterized by low texture, perceptual aliasing, harsh illumination, and

local-aiarxiv-cs-cv

16 bit better than lower quants for Qwen3.6-27B

model-releasesr-localllama

I am writing a fairly complex C++ windows MFC application. I have a few 3090s and can run F16 Qwen3.6-27B with 256K context and MTP. The quality of code is exceptional with this quant vs its lower qua

26 Jul 2026

Mobile Offline LLMs: What do you use them for?

model-releasesr-localllama

I've spent the last year or so playing around with open source MLX and GGUF models on iPhone hardware. Given the limitations in memory, GPU/CPU/ANE, and in turn the context window I've been trying to

25 Jul 2026

The Gemma series is an amazing set of highly performant open-weight models. They have proven extremely effective in industrial settings wher…

model-releasesyann-lecun--x

The Gemma series is an amazing set of highly performant open-weight models. They have proven extremely effective in industrial settings where site-deployed agents need exactly this as a base for domai

25 Jul 2026

Adaptive Confidence-weighted Expansion for Trustworthy Multi-Omics Multimodal Fusion

arXiv:2607.20742v1 Announce Type: new Abstract: Multimodal learning is a robust approach to improve predictive performance in applications such as medical prognosis. However, the clinical applicabilit

safetyarxiv-cs-lg

Agents

// Agentic Context Management // Great read for the weekend. (bookmark it) Production agents fail less on reasoning and more on what sits in…

// Agentic Context Management // Great read for the weekend. (bookmark it) Production agents fail less on reasoning and more on what sits in their context. Conversation history, big prompts, huge tool

agentsdair-ai--x

Agentic Designer: Progressive Multi-Agent Collaboration for Structure-Aware Interior Layout Generation

model-releasesarxiv-cs-cv

arXiv:2607.20866v1 Announce Type: new Abstract: Generating realistic interior furniture layouts that strictly adhere to architectural constraints (e.g., walls, doors, and windows) remains a fundamenta

AI-Driven Multi-Hop Relay Selection for Smart Urban NR-V2X Networks via Learning-to-Optimize Graph Neural Networks

arXiv:2607.20554v1 Announce Type: new Abstract: Reliable and low-latency NR-V2X communications are essential for smart mobility in dense urban environments. However, limited Road-Side Unit (RSU) densi

researcharxiv-cs-ai

Applications

Algorithmic Approaches to Sequential Decision-Making and Social Epistemology

arXiv:2607.20636v1 Announce Type: cross Abstract: As humans, we face many decisions that require us to choose between sticking to something and giving up. This thesis uses algorithmic tools to derive

applicationsarxiv-cs-lg

Automated Synthesis and Adversarial Validation of Executable Causal Research Pipelines

model-releasesarxiv-cs-lg

arXiv:2607.21173v1 Announce Type: new Abstract: While automated research systems promise to accelerate empirical analysis, they are prone to silent failures: instances in which analysis code executes

Benchmarking the Personalization Capabilities of Large Language Models

arXiv:2607.20471v1 Announce Type: new Abstract: Personalization, the act of varying a message to induce action from a specific receiver while keeping sender, channel, and time fixed, has a long tradit

Applications

Beyond Sufficiency: Time Series Explanation with Counterfactual Necessity

arXiv:2607.21573v1 Announce Type: cross Abstract: Faithful explanations of time-series classifiers should identify subsequences that are not only sufficient to preserve a black-box model's prediction,

applicationsarxiv-cs-ai

Black box behavioural modelling: Predicting human activity schedules with a deep conditional generative approach

arXiv:2512.04223v2 Announce Type: replace Abstract: Modelling the complexity and diversity of human activity scheduling behaviour is inherently challenging. We demonstrate ActVAE, a deep conditional-g

CASC: Causal Adversarial Subspace Clustering for Multivariate Spatiotemporal Data

arXiv:2607.21088v1 Announce Type: new Abstract: Deep subspace clustering plays a critical role in applications involving multivariate spatiotemporal data, such as sea ice monitoring, disease spread an

local-aiarxiv-cs-lg

Conflict Resolution under Degraded Surveillance in Air Corridors Using Multi-Agent Reinforcement Learning

arXiv:2607.20547v1 Announce Type: new Abstract: Safe Advanced Air Mobility operations require aircraft to maintain separation when surveillance information is noisy, delayed, incomplete, or temporaril

local-aiarxiv-cs-lg

CRAWO: Custom Resources for Adaptive Workload Orchestration

arXiv:2607.20490v1 Announce Type: new Abstract: Edge Intelligence has emerged as a key paradigm for enabling real-time applications in smart cities by shifting computation from centralized cloud data

researcharxiv-cs-ai

Agents

DAPM: UAV Monocular Depth Estimation from Any Height, Pitch, Roll and FOV

arXiv:2607.21438v1 Announce Type: new Abstract: Monocular depth estimation is a fundamental prerequisite for 3D reconstruction and autonomous navigation in Unmanned Aerial Vehicles (UAVs). In practica

agentsarxiv-cs-cv

DynamicMCPBench: A Trace-Grounded, Effect-Scored Benchmark for LLM Agents over Live MCP Servers

arXiv:2607.20531v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly deployed over Model Context Protocol (MCP) servers, yet the benchmarks used to evaluate them score th

Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference

arXiv:2607.20480v1 Announce Type: new Abstract: Accurate distribution system topology is essential for outage localization, voltage analytics, and operation of distribution grids, yet maintaining reli

local-aiarxiv-cs-ai

Enhancing Explainable Cardiac Diagnosis with Guide-Grounded Multimodal LLMs

arXiv:2607.20814v1 Announce Type: new Abstract: The electrocardiogram (ECG) is a cornerstone of cardiac as- sessment, yet clinical deployment of deep learning models remains con- strained by limited i

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Environment-Aware Channel Inference via Cross-Modal Flow: From Multimodal Sensing to Wireless Channels

arXiv:2512.04966v2 Announce Type: replace-cross Abstract: Accurate channel state information (CSI) underpins reliable and efficient wireless communication. However, acquiring CSI via pilot estimation

safetyarxiv-cs-lg

Expanding Flow Maps

arXiv:2607.21585v1 Announce Type: new Abstract: Flow-based generative models have enabled remarkable progress in fast and controllable generation across continuous and discrete state spaces, yet exist

Agents

Explainable Belief Harmonization under Dynamic Epistemic Partitions

arXiv:2607.21210v1 Announce Type: cross Abstract: Existing approaches to multi-agent belief combination have established mature foundations for combining uncertain beliefs under common assumptions: co

agentsarxiv-cs-ai

Explainable graph attention network for stress recognition (StressGAT) via differential action units

arXiv:2607.20819v1 Announce Type: new Abstract: Stress is a dynamic process characterized by significant individual variability in facial expression. Traditional architectures, such as Recurrent Neura

safetyarxiv-cs-cv

External Clustering Validation by the Homogeneity-Parsimony Trade-off

arXiv:2607.20799v1 Announce Type: new Abstract: Scalar metrics are often used to evaluate clusterings against known classes, but they can obscure a fundamental trade-off: clusterings should be informa

FORGE-plus: Force-Budgeted Recovery for Contact-Rich Assembly with a Frozen LLM Supervisor

arXiv:2607.21227v1 Announce Type: new Abstract: Force-conditioned reinforcement learning (RL) enables tight-clearance assembly under a commanded force ceiling, but practical deployment requires determ

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From Atoms to Entropy: Optimal Noise Allocation for Diffusion Training in the Convex Regime

arXiv:2607.20540v1 Announce Type: cross Abstract: How should a diffusion model decide which noise levels to train on, and how much? Despite the importance of this choice, current noise schedules are b

researcharxiv-cs-ai

From Dependency to Compositionality: A Neurosymbolic Lifting of LLM Outputs via Combinatory Categorial Grammar

arXiv:2607.18961v2 Announce Type: replace Abstract: Large language models (LLMs) generate fluent text by incrementally predicting the next token from a prefix. Critics in the generative tradition argu

local-aiarxiv-cs-ai

GPE: Evaluating Robust Evidence Aggregation for Fact Verification under Controllable GEO-Style Poisoning

arXiv:2607.20730v1 Announce Type: cross Abstract: Large language models increasingly use search tools to retrieve up-to-date information, introducing a new attack surface in which retrieved documents

GroupVideo: Multi-Identity Customized Text-to-Video Generation

arXiv:2607.21027v1 Announce Type: new Abstract: Current identity customized video generation methodologies are predominantly limited to single-identity scenarios, as the lack of explicit identity sepa

safetyarxiv-cs-cv

Is Deep Research Reliable? Misleading Knowledge Induces False Conclusions

arXiv:2607.20891v1 Announce Type: new Abstract: Deep Research agents extend LLM-based assistants into long-horizon workflows involving planning, retrieval, evidence synthesis, and report generation, y