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Agent Harness vs Agent Framework vs MCP: Which Layer Owns the Loop, State, Tools, Permissions, and Recovery

Architecture explainer separates agent harnesses, frameworks, and MCP by which layer owns the loop, state, permissions, and recovery.

The article distinguishes agent harnesses (OpenAI Codex, Claude Agent SDK), which own the execution loop, sandbox, permission model, and recovery; frameworks (LangGraph, OpenAI Agents SDK, Microsoft Agent Framework), which supply composable primitives; and MCP, a stateless JSON-RPC wire protocol governed by the Linux Foundation's Agentic AI Foundation since December 2025. An ownership matrix maps the execution loop, state, tool transport, permissions, recovery, sandboxing, and multi-agent orchestration to each layer. The 2026-07-28 MCP specification made the protocol fully stateless, retiring the initialize handshake and session headers.

MarkTechPost · 2d agoAI research1

Tracking the Unseen: An Occlusion-Robust Framework for Target Tracking Under Full and Long-Term Occlusion

Occlusion-robust tracker combining YOLOv11n, Kalman filtering, and appearance Re-ID cuts identity switches and beats OccluTrack by 18.1% MOTA on OVIS.

The framework integrates YOLOv11n object detection, Kalman Filter motion prediction, and occlusion-aware appearance-based re-identification to maintain target identity through full and long-term occlusion. Six Re-ID architectures were evaluated under identical conditions, with the Occlusion-Aware Mask Network (OAMN) performing best. On the public OVIS dataset it improves MOTA by 18.1% and IDF1 by 25.1% over OccluTrack while reducing identity switches by 12.8%; on a custom military surveillance dataset it achieves MOTA 0.734 and IDF1 0.729.

arXiv cs.AI / cs.LG / cs.CL · 1d agoAI research

TFTrack: A Template-Free Framework for Efficient 3D Point Cloud Tracking

Researchers propose TFTrack, a template-free LiDAR 3D single object tracking framework cutting FLOPs ~50% while running at ~120 FPS.

TFTrack is the first template-free framework for 3D Single Object Tracking, dropping template-search pairings and complex motion modeling in favor of the prior bounding box center plus geometric alignment. It ships in three variants (TFTrack-Voxel, TFTrack-Pillar, TFTrack-Point) covering sparse and dense 3D representations. On KITTI and nuScenes it is competitive with leading template-based trackers while reducing FLOPs by about 50% and running near 120 FPS. Code is released, targeting real-time deployment in embedded robotics such as autonomous vehicles.

arXiv cs.AI / cs.LG / cs.CL · 9d agoAI research

One Editor, Many Edits: A Unified Training-Free Framework for Diverse Video Editing

Researchers introduce EditVid, a training-free video editing framework scoring 78.16 FiVE-Acc versus 58.95 for the strongest comparable baseline.

EditVid is a unified training-free framework for diverse instruction-guided and subject-guided video editing. It combines sparse causal memory for local coherence, correspondence-based post-attention token injection for long-range identity preservation, and soft latent blending for edit locality. On the FiVE benchmark it reaches 78.16 FiVE-Acc against 58.95 for the strongest evaluated training-free baseline, with competitive results on IVEBench. A user study showed 51.8% overall preference for EditVid over 7 competing methods.

Hugging Face daily papers · 14d agoAI research

EmbodiedSkills: A Unified Framework for Orchestrating, Training, and Deploying VLA Agents

Researchers introduce EmbodiedSkills, a framework treating VLA skill decisions as verified execution proposals, reaching 86.2% success on RoboTwin 2.0.

The EmbodiedSkills framework treats each vision-language-action skill decision as an execution proposal, checking prerequisites before execution and verifying outcomes afterward via a shared executable-skill interface. It connects high-level skill selection, bounded low-level VLA execution and post-action verification in a single agent loop, and logs structured trajectories for supervision and optional online adaptation. Instantiated with Qwen3-VL and OpenPI/pi0.5, task-adapted policies achieve 86.20% average success across 50 RoboTwin 2.0 tasks and 97.40% across the four LIBERO suites, with 12.5% on memory-dependent RMBench tasks.

Hugging Face daily papers · 16d agoAI research1

Before You Poll with LLMs: A Deliberative Diagnostic Framework

Deliberative diagnostic shows all five tested frontier LLMs misrepresent human belief shifts after arguments, with GPT-5.1 reversing on outgroup questions.

The Deliberative Polling Diagnostic Framework compares human and LLM persona belief shifts after identical informational interventions, using data from America in One Room (526 personas, 72 questions). All five frontier models tested failed uniquely: GPT-5.1 exhibited partisan reversal (80% on outgroup vs 26% on policy questions), Gemini 2.0 Flash, Claude Sonnet 4.5 and Llama 3.3 70B overshot at 5-7x human magnitude, and DeepSeek V3 showed near-zero change (rigidity). The authors term the underlying signature 'self-sycophancy', conformity to the model's internal persona stereotype rather than reasoning from provided information.

arXiv cs.AI / cs.LG / cs.CL · 2d agoAI research

Generalized Agent Iteration: One Formal Framework for Iterative Policy Improvement and Recursive Self-Improvement

Generalized Agent Iteration formally unifies iterative policy improvement and recursive self-improvement, defining axes that distinguish anchored, goal-drifting, and self-referential agents.

The paper proposes Generalized Agent Iteration (GAI), a formal framework that models learning as a cycle of agent evaluation and agent improvement, defining the agent as a configuration of modifiable components. Two dials—whether the improving mechanism is part of the agent and whether the evaluation standard is grounded outside it—separate generalized policy iteration (GPI) from recursive self-improvement (RSI) and classify systems as anchored, goal drift, or fully self-referential. The framework places existing systems on shared axes and makes defects of recursive self-improvement statable one condition at a time.

Hugging Face daily papers · 6d agoAI research1

ReactVAU: A Slow-Fast Decoupled Framework for Streaming Video Anomaly Understanding

ReactVAU is a slow-fast streaming framework for real-time video anomaly understanding that reserves heavyweight MLLM reasoning for suspicious events, improving efficiency.

ReactVAU addresses causal streaming video anomaly understanding with three components: a lightweight Fast Detection Module using Spatial Grid Folding, Anomaly-Aware Persistent Memory that protects critical visual cues from temporal decay, and a Slow Reasoning Module activated only on suspicious events. This design minimizes heavyweight MLLM invocations during long normal intervals. Experiments show competitive anomaly detection and causal reasoning under strict streaming constraints with significantly enhanced computational efficiency.

Hugging Face daily papers · 10d agoAI research

A Hybrid LSTM-XGBoost Framework for Multi-Horizon Stock Return Prediction Across Diversified Equity Portfolios

Hybrid LSTM-XGBoost model predicts multi-horizon returns for 14 US equities, cutting 30-day RMSE to about one-third of a standalone LSTM baseline.

The paper combines a two-layer LSTM (64 hidden units) processing 60-day windows of five market features with an XGBoost regressor over a 78-dimensional hybrid feature vector including 14 technical indicators. It is trained on pooled data for 14 US equities across six sectors using chronological splits and per-stock MinMaxScaling to prevent look-ahead bias, and evaluated at 30, 90, 252, and 365 trading-day horizons. The hybrid achieves test RMSE of 0.0949 at 30 days, roughly one-third of the standalone LSTM, while 97.6% directional accuracy at 365 days largely tracks the base rate of positive returns.

arXiv cs.AI / cs.LG / cs.CL · 5d agoAI research

Google Research Releases ToolGrad: Answer-First Framework Hits 99.8% Pass Rate for Tool-Use Data Generation

Google Research and partners introduce ToolGrad, a verified tool-chain-first data generation framework reaching 99.8% pass rate and boosting Gemma-3-12B to 83.1 on BFCL.

Researchers from Google, the University of Tokyo, RIKEN AIP, and Tohoku University released ToolGrad, which inverts query-first tool-use data generation by executing and verifying API chains before annotating them with user queries. On the ToolBench database of 16,000+ APIs, ToolGrad raised generation pass rate from 63.8% to 99.8% while increasing tool uses per sample from 2.1 to 3.4 and cutting tool-use steps from 34.3 to 20.0. Fine-tuning Gemma-3 at 1B, 4B, and 12B parameters on the 500-sample ToolGrad-500 dataset lifted ToolGrad-12B to 83.1 on the Berkeley Function Calling Leaderboard, near Gemini 2.5 Pro at 83.2 and ahead of GPT-5 at 74.4. Code is Apache-2.0, with the dataset, PyPI package, and models available on Hugging Face.

MarkTechPost · 6d agoAI research1

Privacy-enhanced federated learning via asynchronous aggregation and local differential perturbation

Federated learning framework combining dynamic differential privacy, homomorphic encryption, and local DP retains 82.6% accuracy at epsilon 0.1 while cutting communication 21.3%.

The paper proposes a privacy-enhanced federated learning framework integrating Dynamic Differential Privacy, lightweight Homomorphic Encryption, and Local Differential Privacy during training. An asynchronous aggregation strategy with version control supports distributed training in asynchronous environments. On CIFAR-10 and Purchase-100, the method maintains up to 82.6% classification accuracy under stringent privacy constraints (epsilon = 0.1) and reduces communication overhead by 21.3% versus FedAvg.

arXiv cs.AI / cs.LG / cs.CL · 2d agoAI research

ASTRIL-MPC: Autonomous Traversal Framework of Articulated Tracked Robots with Language-Guided Neural-Kinematic MPC

ASTRIL-MPC combines learned kinematics, NMPC, and LLM-guided safety-checked retuning for articulated tracked robot traversal in search-and-rescue.

ASTRIL-MPC is a language-guided neural-kinematic model predictive control framework for autonomous traversal of articulated tracked robots in urban search and rescue. A learned kinematics model predicts short-horizon task-state increments, NMPC plans with feasibility constraints, and an LLM proposes bounded, safety-checked updates to weights and bounds. The compiled predictor enables a full control cycle within 100 ms, improving traversal-quality scores by up to 71% over non-adaptive NMPC and 67% over a PPO baseline while eliminating measurable collision impacts.

arXiv cs.AI / cs.LG / cs.CL · 5d agoAI research

OmniMed-FL: A Robust Multimodal Federated Learning Framework for Clinical Diagnosis

OmniMed-FL benchmarks multimodal federated learning for chest radiograph diagnosis across 3-20 clients, with FedProx leading under severe non-IID skew.

OmniMed-FL studies multimodal federated learning combining chest radiographs and clinical notes for five-class condition classification under HIPAA/GDDR-compliant decentralized training. It benchmarks eight fusion strategies, imputation rules, and federated baselines under Dirichlet non-IID partitioning across 3-20 hospital clients. With 5 clients and severe skew (alpha=0.1), FedProx scored 0.737 macro-F1 versus 0.662 for FedAvg and 0.297 for local-only training. Multimodal fusion beat unimodal inputs (0.956 vs 0.934 text, 0.664 images) on the synthetic corpus.

arXiv cs.AI / cs.LG / cs.CL · 7d agoAI research1

A General Kernel Framework for Non-CND Distance Measures Using |D|-Dimensional Sparse Landmark Embeddings

Proposes the Sparse Landmark Embedding kernel, guaranteeing PSD kernels for arbitrary distances like geodesic and Wasserstein without CND requirements.

The paper introduces the Sparse Landmark Embedding (SLE) kernel, which embeds inputs via compactly supported bump functions at all |D| training points so any standard PSD kernel applies, removing the Hilbertian (CND) distance requirement that fails on manifolds and distribution spaces. Compact support controls sparsity, keeping kernel matrices well-conditioned despite high dimensionality. The authors prove PSD, sparsity, stability, and universal approximation guarantees, and show SLE matches or exceeds domain-specific baselines using geodesic and Wasserstein distances on accuracy and uncertainty quantification.

arXiv cs.AI / cs.LG / cs.CL · 19h agoAI research

Structured Claim-Level Discourse Representations for Dense Health Narratives

Researchers propose a claim-level discourse framework for health videos, finding 13.22 atomic claims per minute and that LLMs struggle with pragmatic profiling.

The paper introduces a structured framework for claim-level discourse analysis in dense health narratives on social media videos, modeling tuples that link atomic claims with thematic aspects, stance, and multidimensional pragmatic attributes. Analysis found an average of 13.22 atomic claims per minute in health video discourse. A benchmark spanning four health domains with 1,191 manually annotated claims from 60 videos shows current LLMs perform strongly on thematic categorization and stance prediction but struggle with high-dimensional pragmatic profiling, suggesting future systems need task decomposition and specialized inference strategies.

arXiv cs.AI / cs.LG / cs.CL · 20h agoAI research

Generative Marketing Mix Modeling: A Causal Inference Framework Linking GEO and GEM to Business Impact

Paper proposes Generative Marketing Mix Modeling to causally estimate Generative Engine Optimization and Marketing effects on business outcomes.

The authors develop GMMM, a causal inference framework for measuring how often users see and notice a firm's name in generated answers, which standard marketing data ignore. For GEO it combines repeated generated answers with question counts, shares of generative-system usage and notice probabilities; for GEM it uses sponsored placement records with notice probabilities. The framework compares expected business responses under alternative treatment sequences, establishes identification conditions, and is evaluated on simulated product-recommendation answers in English and Japanese.

arXiv cs.AI / cs.LG / cs.CL · 6d agoAI research

Comprehensive reconstruction of collider events with hypergraph representation learning and graph-conditioned diffusion

VyPER framework reconstructs collider events using hypergraph representation learning and graph-conditioned diffusion, outperforming existing reconstruction techniques across Standard Model processes.

Researchers present VyPER, a geometric learning framework that represents collider events as hypergraphs with a physics-inspired topology for particle event reconstruction. It combines supervised hyperedge classification for assigning measured jets and charged leptons to parent particles with a graph-conditioned diffusion model predicting unmeasured neutrino kinematics, optimized with a joint loss. Evaluated across several proton-proton collision processes, it demonstrates accurate reconstruction across Higgs, electroweak, and top-quark sectors.

arXiv cs.AI / cs.LG / cs.CL · 19h agoAI research

Anchoring Clinical Events in Time: UID-Preserving Multimodal Reconstruction and Source-Grounded Adjudication

UID-preserving multimodal framework plus GAVEL LLM judge improves clinical timeline reconstruction, boosting event recovery 43% over prior matching.

The paper introduces a UID-preserving framework linking each narrative clinical event to its source span through text-only estimation, structured-evidence retrieval, timestamped source-row grounding, and joint revision. GAVEL, an LLM judge, compares UID-aligned timelines against narrative and structured records. Across six open-weight models and 40 mixed-critical-care summaries, GLM 5.2 multimodal revision improved temporal agreement without reducing event recovery and performed competitively with clinician annotations, while DeepSeek V3.2 did not benefit from multimodality. The pipeline achieves 43% increased event recovery with occurrence-level provenance.

arXiv cs.AI / cs.LG / cs.CL · 5d agoAI research1

Algorithmic stability via ensembling

Theoretical work derives a general framework quantifying stability guarantees for averaging-based ensembles under arbitrary data perturbations via covariance operator norms.

The paper develops a framework for quantifying algorithmic stability of ensembling strategies defined via averaging, for varied types of data perturbation. The main result bounds the stability of the ensembled algorithm in terms of the norm of a covariance operator describing the ensembling process. The framework yields interpretable insights across practical perturbation examples and provides sharper guarantees than those derived from differential privacy considerations.

arXiv cs.AI / cs.LG / cs.CL · 7d agoAI research

Think Before You Link: Rarity, Reasoning, and Retrieval in Multilingual Entity Linking

Training-free reasoning-plus-retrieval framework lifts multilingual multimodal entity linking accuracy by 6.9% overall and up to 23.3% on rare entities.

The paper broadens rarity measurement in multimodal entity linking using knowledge-graph structural metrics beyond popularity-based pageview metrics, identifying many rare entities that popularity metrics miss. Across these rare-entity slices, state-of-the-art accuracy drops by 15.4-39.9%, exposing distinct failure modes under different rarity definitions. The proposed training-free framework iteratively searches and reasons over Wikipedia with a reasoning-capable vision-language model; experiments show reasoning and retrieval are complementary. On the MERLIN benchmark covering Hindi, Indonesian, Japanese, Tamil, and Vietnamese, the system improves overall accuracy by 6.9% and up to 23.3% on rare entities, with MERLIN-Rare test slices released.

Hugging Face daily papers · 8d agoAI research1

ReCite: Agentic Reasoning for Faithful Citation

ReCite is an agentic citation framework using claim-level reasoning and verification, outperforming large generative models in strict citation accuracy.

ReCite is a decoupled agentic framework that orchestrates location perception, intent-aware query planning, and reflective verification for citation recommendation. Trained on synthesized reasoning trajectories, the agent verifies claim-evidence consistency and triggers self-correction loops when retrieved candidates lack logical support. Experiments show the lightweight framework outperforms state-of-the-art massive generative models in strict citation accuracy, addressing misattribution where cited papers are real but logically unsupportive.

arXiv cs.AI / cs.LG / cs.CL · 8d agoAI research

PlannerForge: LLM Agents for Scenario-Based Testing of Motion Planners in Autonomous Driving

PlannerForge unifies scenario-based testing of autonomous driving motion planners in one LLM-agent framework, outperforming prior baselines.

PlannerForge is an LLM-agent framework that covers the full scenario-based testing pipeline for autonomous driving systems, spanning scenario generation, selection, modification, routing, planner testing, plus new enhancement and benchmarking stages. In evaluations with 10 off-the-shelf LLMs, best-per-task scores range from 0.88 to 1.00, and open-source 20-35B backends such as Qwen3.6:35B match commercial APIs on most tasks. End-to-end chaining retains 83% (commercial) and 78% (open) of seed queries, beats Scenario Factory 2.0 on executable generation, and cost-tuning lifts planner success from 50.4% to 70.2% while cutting collisions from 19.0% to 8.4%.

Hugging Face daily papers · 9d agoAI research

Guiding Worker Self-Selection in Crowdsourcing Contests: An LLM-Augmented Algorithmic Approach

Researchers introduce GRAF, a greedy framework for crowdsourcing contest self-selection, and LLMScore, an LLM-driven method that auto-designs its scoring algorithm.

The paper studies self-selection in Tullock contests (SSTC), where workers choose contests and then compete within them. GRAF is a greedy polynomial-time framework that orders workers by a score vector with zero worker regret and platform optimality guarantees in special cases. LLMScore is an LLM-driven evolutionary framework that produces human-readable, inspectable scoring code, jointly optimizing platform utility and worker satisfaction. Across 1,000 synthetic instances in four settings, GRAF with LLMScore achieves high-quality, often near-optimal outcomes with low worker regret, transferring from small training instances to larger, structurally different settings.

arXiv cs.AI / cs.LG / cs.CL · 9d agoAI research1

To See a World in a Living Context: Unified Indoor-Outdoor Urban World Generation

Researchers introduce HoloWorld, a unified text-driven framework generating coherent indoor-outdoor 3D urban worlds, improving average AQS over SOTA by 7.68%.

HoloWorld is a text-driven 3D generation framework that unifies indoor and outdoor urban world generation using a continuously updated cross-scale world context. It autoregressively generates urban exteriors with consistent spatial organization, grounded in 3D building instances and footprints, then produces building-specific interiors with geometry-constrained layouts that inherit exterior appearance. The authors claim it is the first framework to unify indoor and outdoor generation within one coherent 3D urban world, reporting a 7.68% average AQS improvement over prior SOTA and the highest average RDR score.

Hugging Face daily papers · 19d agoAI research

Real-World Knowledge-Guided Change Data Synthesis for Remote Sensing

Researchers introduce KnowChange, a framework that uses pretrained vision-language models to synthesize realistic change-detection training data for remote sensing.

KnowChange is a knowledge-guided change data synthesis framework that leverages pretrained vision-language models to reason about plausible change locations and class transitions from pre-change scenes and desired change types. It addresses the limited class-transition coverage and inflexibility of handcrafted rule-based synthesis methods, enabling diverse change types in a unified pipeline. Experiments show KnowChange-generated data outperforms existing synthetic datasets in both synthetic-to-real transfer and synthetic data augmentation, despite compact generation scale.

Hugging Face daily papers · 23d agoAI research

Probabilistic Linear Explanations

Researchers introduce a unified probabilistic explainability framework using sparse anchored linear models that outperforms LIME and MAPLE on relevance error.

The paper proposes probabilistic explanations based on sparse, anchored linear models applicable to both binary classification and continuous regression. It proves that minimizing relevance error for neural-network models is NP-hard and relates it to a tractable fidelity-error surrogate. Solutions are computed via a mixed integer programming formulation with provably optimal empirical solutions and a polynomial-time iterative hard thresholding algorithm with approximation guarantees. Empirical evaluations show lower relevance error than LIME and MAPLE while satisfying anchoring and sparsity constraints by construction.

arXiv cs.AI / cs.LG / cs.CL · 19h agoAI research

FlashVector: Agent for Hierarchical Model Serving Stack Optimization

FlashVector agent optimizes all layers of Unity's ad-serving stack, delivering up to 2x model-server throughput and 1.98x latency speedup in production.

FlashVector is an agentic system that optimizes performance across GPU kernels, ML framework computation graphs, model servers, and on-demand feature processing. Deployed in Unity's Vector advertising platform, it achieved up to 2x model-server throughput increase, 1.98x latency speedup, and 1.6x feature-store throughput gain. Optimizations spanned NVIDIA Triton's C++ codebase and the Python feature transformation service, demonstrating extensibility beyond single-kernel tuning.

arXiv cs.AI / cs.LG / cs.CL · 1d agoAI research1

Safe Meta-Reinforcement Learning via Information Space Reachability

Safe meta-RL framework reasons about safety in information space, learning a safety value function used for safety filtering and constrained policy optimization.

The paper proposes safe meta-RL that reasons about safety in information space, capturing both physical state and the agent's belief over the underlying task. A safety value function measures the probability of avoiding unsafe regions indefinitely and satisfies a self-consistency condition and Bellman equation, making it learnable via meta-RL. The resulting algorithm uses the learned function for safety filtering and constrained policy optimization, with effectiveness demonstrated on meta-RL benchmarks.

arXiv cs.AI / cs.LG / cs.CL · 2d agoAI research

RSIAgent: Autonomous Exploration for Recursive Self-improvement in New Environments

RSIAgent, a training-free multi-agent framework, builds reusable environment memory enabling Kimi-K3 and GLM-5.3 to beat GPT-6.

RSIAgent is a training-free framework for recursive self-improvement through autonomous memory construction, coordinating curriculum, actor, and verifier agents. It uses broad-then-deep exploration to capture environment structures, hidden constraints, and causal dependencies, and freezes the resulting memory for direct reuse without parameter updates. On OSWorld-v2 and Agent's Last Exam it substantially improves strong open-source models, enabling Kimi-K3 and GLM-5.3 to outperform frontier closed-source models including GPT-6.

Hugging Face daily papers · 3d agoAI research2