Search: “model-releases”

Lens: Bringing the Right Semantic Perspective into Focus for Training-Free Multimodal Representation Learning

arXiv:2609.20252v1 Announce Type: cross Abstract: High-quality representations are essential for a wide range of downstream tasks. Dedicated embedding models are explicitly optimized for representatio

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Model Releases

Less Is More: Graph-free Multimodal RAG via Multi-signal Late Fusion

DGX agent

arXiv:2609.19417v1 Announce Type: new Abstract: Graph-based retrieval-augmented generation (RAG) is widely used for multimodal, cross-document question answering. However, building corpus-level graphs

model-releasesarxiv-cs-cl

18 Sept 2026

LinePilot Digitizer: Line-Plot Recovery with Manual and Automatic Calibration

arXiv:2609.19377v1 Announce Type: cross Abstract: Recovering numerical series from line plots requires accurate axis calibration and reliable curve extraction. We present LinePilot Digitizer (LinePilo

Marginal utility, matrix factorization, and the Key-Value (KV) cache: a unified information-economic framework for sovereign geo-mining inference

arXiv:2609.20068v1 Announce Type: new Abstract: This paper builds a theoretical bridge between the economic notion of marginal utility and two machine-learning constructs, matrix factorization and the

MaSCoD: A Multi-Agent Framework for Structural-Context-Guided Candidate Causal Graph Generation

arXiv:2609.19944v1 Announce Type: new Abstract: Large language models (LLMs) have been applied to causal discovery, but candidate-graph generation rarely treats premature omission of potentially relev

MeshKV: A Network-on-Chip KV Cache Fabric for Scalable Transformer Decoding Accelerators

arXiv:2609.19207v1 Announce Type: cross Abstract: Autoregressive transformer decoding is constrained by irregular key-value (KV) cache movement on tiled accelerators. Prior compression and DRAM-placem

Message capacity and claim wording set the transition points of collective truth-finding in language-model networks

arXiv:2609.19183v1 Announce Type: cross Abstract: Whether human or large language model (LLM), an agent in a discussion reads only a few of the others' contributions, bounded by cognition, context, or

Minimax-Optimal Online Contract Design with Unrestricted Bounded Contracts

arXiv:2609.20353v1 Announce Type: new Abstract: We study repeated contract design when a principal observes outcomes but not the actions that generate them. The principal may use any bounded outcome-c

MTVA-Bench: Evaluating the Language Model Inside Cascaded Voice Agents

arXiv:2609.20152v1 Announce Type: new Abstract: Generally, most voice agents are cascaded systems, i.e., an ASR model transcribes the caller's audio, a language model reads the transcript and decides

Multi-Dimensional Prosody Judgment For Live Streaming Speech Synthesis

arXiv:2609.20124v1 Announce Type: cross Abstract: Evaluating live streaming speech synthesis (TTS) requires assessing fine-grained, highly expressive prosody such as emotion, intonation, and energy wh

Navi-Agent: Unlocalized Monocular Navigation Agent

arXiv:2609.20388v1 Announce Type: cross Abstract: Vision-Language Navigation in Continuous Environments (VLN-CE) requires an embodied agent to execute long-horizon instructions in unknown environments

Neo-Classic: A Benchmark for Evaluating Linguistic-Aesthetic Reasoning in Classical Chinese Poetry

arXiv:2609.19154v1 Announce Type: new Abstract: While Large Language Models (LLMs) achieve high accuracy on established Classical Chinese Poetry benchmarks, it remains challenging to distinguish trans

NeuSOGA3D: A Neuro-Symbolic Framework for Explainable 3D Geometric Reconstruction

arXiv:2609.20323v1 Announce Type: new Abstract: Three-dimensional reconstruction from unorganized point clouds remains a challenging problem in computer vision, geometric modeling, and computer-aided

Not All Nodes Are Created Equal: Homophily-Aware Stratification for Stable GNN Evaluation

arXiv:2609.19210v1 Announce Type: cross Abstract: Graph neural networks are widely used for transductive node classification, with accuracy typically measured on randomly drawn train/validation/test s

OmniCalib: Target-Free, Task-Structured Self-Calibration for Humanoid Robots

model-releasesarxiv-cs-ro

arXiv:2609.19582v1 Announce Type: new Abstract: Assembly, wear, and component replacement perturb the sensor extrinsics and joint zeros encoded by a humanoid CAD model. Existing procedures calibrate o

On-Demand Attention: Language Models Know When to Recall

arXiv:2609.20734v1 Announce Type: new Abstract: Reasoning and agentic workloads increasingly demand efficient long-context inference. Yet full-attention decoding reads the growing history at every ste

Online Adaptation of Visual Odometry Frontends with Image-Conditioned Reinforcement Learning

arXiv:2603.21785v2 Announce Type: replace Abstract: Visual odometry (VO) frontends are typically tuned offline by domain experts on pre-recorded datasets and then deployed with fixed hyperparameters.

Paint-Anything: Unified Any-Color Control for Image Generation and Editing

arXiv:2609.20816v1 Announce Type: cross Abstract: Professional design requires any-color control: the ability to specify an object's target color with any 24-bit hex value for image generation and edi

Past, Future, All at Once: Mitigating Stability-Plasticity Dilemma via Post-hoc JANUS Rectification

arXiv:2609.19985v1 Announce Type: cross Abstract: Fine-tuning foundation models on new tasks inevitably suffer from catastrophic forgetting. While existing works attempt to mitigate this on the basis

Perception, Layout, and Validation: Calibrated Confidence for Reliable Straight-Through Processing of Financial Documents

arXiv:2609.20110v1 Announce Type: new Abstract: Straight-through processing (STP) on extracted key-value fields from financial documents without human review requires a calibrated probability together

PersonalAI 2.0: Enhancing knowledge graph traversal/retrieval with planning mechanism for Personalized LLM Agents

arXiv:2605.13481v2 Announce Type: replace Abstract: We introduce PersonalAI 2.0 (PAI-2), a novel framework designed to enhance LLM-based systems through integration of external knowledge graphs (KGs).

PetriBench: Benchmarking LLM Reasoning over Dynamic State Spaces

arXiv:2609.19883v1 Announce Type: cross Abstract: Characterizing LLM reasoning remains an open challenge, as many existing benchmarks isolate specific reasoning skills, rely on external knowledge, or

Placement Is Free, Composition Is Not: The Latin Square as a Provably-Balanced Construction for Heterogeneous Sequence-Mixer Stacks

arXiv:2609.20269v1 Announce Type: new Abstract: Since GPT, most Transformers have repeated the same attention mechanism at every layer. Yet this design is largely a convention rather than a tested con

PosteriorBench: From Point Estimates to Posterior Matching in Evaluating Generative Inverse Solvers

arXiv:2609.20794v1 Announce Type: new Abstract: Generative models are increasingly used to solve scientific inverse problems, but existing evaluations still focus primarily on whether a method can pro

Predict Before You Deploy: Offline Prediction of Quantization-Induced Task Degradation for World Action Models

arXiv:2609.19441v1 Announce Type: cross Abstract: World action models (WAMs) rely on video-generation backbones, requiring substantial memory and compute for deployment. Post-training quantization red

Prediction-Powered Smoothing and Validation for Disaggregated AI Evaluation

arXiv:2609.20758v1 Announce Type: cross Abstract: Evaluating an AI system requires disaggregated assessment, as performance varies across domains such as benchmark task types or conversation types in

PrefixBench-H100: Characterizing Prefix Reuse and Time-to-First-Token in H100 LLM Serving

arXiv:2609.19657v1 Announce Type: cross Abstract: Repeated prompt prefixes are increasingly common in LLM serving workloads, appearing in system prompts, templated retrieval-augmented generation pipel

QCPruner: Query-Conditioned Population Coverage for Visual Token Pruning

arXiv:2609.19990v1 Announce Type: new Abstract: The high visual-token load in multimodal large language models (MLLMs) motivates training-free pruning to reduce later-layer computation, but under a fi

Quantifying Mechanical Intelligence in Legged Robots with Information Theory

model-releasesarxiv-cs-ro

arXiv:2609.19588v1 Announce Type: new Abstract: Mechanical intelligence, loosely defined as the reduction in control burden afforded by a robot's physical form, has become a prominent concept in robot

Quantum Graph Convolutional Networks: Implementation and Trainability Analysis

arXiv:2609.19983v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) achieve state-of-the-art performance on graph-structured data, but training and inference on large graphs are often bottl

QVAC Genesis III: A Large-Scale, High-Quality Open Synthetic STEM Corpus for Efficient Language Model Pre-Training

arXiv:2609.19513v1 Announce Type: new Abstract: High-quality pre-training data is a critical bottleneck for educational and STEM-specific language models targeting edge AI and on-device deployment whe

RAFT: A Stateful Retrieval-Augmented Framework for Troubleshooting Agents

arXiv:2609.20754v1 Announce Type: new Abstract: Effective troubleshooting agents in enterprise customer support depend on retrieving actionable guidance from similar historical cases, yet existing ret

Reach or Solve? Attributing Agentic RL Gains with Checkpoint Handoffs

arXiv:2609.19636v1 Announce Type: new Abstract: Reinforcement learning now trains language-model agents that act over dozens of steps in live environments. The gains are large, and they are read as be

Red-Teaming Auto Mode: Improving Blocking Classifiers Against Malign Coding Agents

arXiv:2609.19587v1 Announce Type: cross Abstract: To keep coding agents from going off the rails, production systems now review each proposed action with a blocking monitor that can reject it before i

Redemption Score: A Multi-Modal Evaluation Framework for Image Captioning via Distributional, Perceptual, and Linguistic Signal Triangulation

arXiv:2505.16180v3 Announce Type: replace-cross Abstract: Evaluating image captions requires cohesive assessment of both visual semantics and language pragmatics, which is often not entirely captured

Reflective Recovery: A Self-Supervised Method for Reasoning by Learning from Mistakes

arXiv:2609.19156v1 Announce Type: new Abstract: Data-driven fine-tuning is widely adopted to enhance reasoning in Large Language Models (LLMs) due to its simplicity and efficiency. However, mainstream

Replan, Repair, or Edit? A Unified Empirical Evaluation of Travel Agents for Itinerary Revision under Resource Disruptions

arXiv:2609.19654v1 Announce Type: new Abstract: Travel-planning agents generate itineraries that may become infeasible after acceptance because of flight cancellations, hotel unavailability, or attrac

Riemannian--Lorentz Fusion of Vision Transformers and State-Space Models

arXiv:2609.19384v1 Announce Type: cross Abstract: Scaling deep learning faces critical bottlenecks: data exhaustion, exponential training costs, and resource concentration. Model merging combines pre-

SAFARI: An Industrial Benchmark for LLM-Assisted Hazard Analysis and Risk Assessment

arXiv:2609.20584v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly considered for safety-critical engineering, yet their reliability in regulated functional-safety workflows

Safety Beyond the Interface: Detecting Harm via Latent States in Large Language Models

arXiv:2609.19472v1 Announce Type: new Abstract: Autonomous systems increasingly rely on Large Language Models (LLMs) yet the safety infrastructure surrounding these models introduces latency and compu

SAGG: Sample-Adaptive Gradient Gating for Robust Multimodal Learning under Heterogeneous Corruption

arXiv:2609.20302v1 Announce Type: new Abstract: Multimodal gradient balancing methods modulate encoder gradients with a shared scalar per modality, implicitly assuming that corruption is uniform acros

Sample Count Is Not Enough: Candidate-Generation Strategy Shapes the Energy and Performance of LLM Test-Time Scaling

arXiv:2609.19499v1 Announce Type: new Abstract: Test-time scaling can improve large language model reasoning by generating and combining multiple candidate responses. In sampling-based methods, the in

Sampling Reveals Style: Unsupervised, Training-Free Discovery of Prompt-Conditional Stylistic Axes in LLM Activations

arXiv:2609.19150v1 Announce Type: new Abstract: Large language models (LLMs) encode rich stylistic structure in their hidden activations, but discovering which stylistic dimensions are salient for a g

Scalable Policy Optimization for Networked Multi-Agent Reinforcement Learning with Continuous State-Action Spaces

arXiv:2607.18554v2 Announce Type: replace-cross Abstract: Learning local policies for continuous networked systems requires accounting for the effects of decisions beyond each agent's observation neig

SceneTeract: Probing and Improving Agent-Aware Activity Reasoning in 3D Indoor Scenes

arXiv:2603.29798v2 Announce Type: replace Abstract: Indoor 3D scenes are ultimately meant to be used: an embodied agent should be able to navigate, reach objects, and complete diverse activities. Yet

ScientistTwo: Pioneering the Human Knowledge Frontier with Autonomous AI

arXiv:2609.19644v1 Announce Type: new Abstract: Scientific discovery is defined by the ability to identify the boundaries of existing knowledge and venture into unexplored territory. The ultimate visi

SCOUT: Sim-to-Real Text-Based Person Retrieval by Embedding-Space Prediction over Frozen Video Features

arXiv:2609.19483v1 Announce Type: new Abstract: Text-based person retrieval under a sim-to-real gap (synthetic training data, a real-image gallery) is usually tackled with costly fine-tuned cross-enco

Self Improvement via Fast Tree-search

arXiv:2609.19526v1 Announce Type: new Abstract: Coding agents can recursively modify their own implementations, forming a loop of self-improvement. While prior work shows this can boost performance on