Harness-Zero folds scaffolds into weights, IER hits 0.1%, RRSI prunes harnesses
Every URL the pipeline pulled into ranking for this issue — primary sources plus the supporting and contradicting findings each Researcher returned. Inline citations in the issue point back here.
Sources
RRSI: Regularized Recursive Self-Improvement of Agent Harnesses huggingface.co
An LLM agent’s capability is largely magnified by its harness, namely the prompts, control flow, tooling, memory, and context management surrounding the frozen backbone model. Recent methods increasingly automate this process by iteratively proposing and selecting component-wise edits of an agent harness, practically establishing a form of recursive self-improvement (RSI) at the agent-system level. However, such recursive evolution may overfit by memorizing the training tasks, showing large in-d
1% of Tokens Can Be Enough: On Gradient Estimation in On-Policy Distillation huggingface.co
Sparse on-policy distillation (OPD) allocates teacher supervision to a small subset of tokens in student-generated trajectories. However, useful teacher guidance can yield a noisy update when its gradient is estimated from a sampled next token. We study this estimation problem at a fixed prefix in information geometry and propose an information-efficiency ratio (IER) based on a signal-to-noise decomposition. IER characterizes relative gradient estimation error under an optimal scalar baseline. A
Harness-Zero: Harness Distillation via Agent-as-Harness huggingface.co
Agent harnesses, the external systems that mediate model-environment interaction, can substantially improve agent performance, but their gains remain tied to the harness at deployment. Because the best harness varies across domains, instances, and models, a general-purpose agent must either settle for a suboptimal shared harness or route among an ever-growing set of specialized ones. We therefore study agent harness distillation: using a domain- or instance-optimized harness as training-time gui
Jev-Mem: System-One-Controlled Agentic Memory for Efficient AI Agents huggingface.co
Jev-Mem borrows Kahneman’s System-One/System-Two split to cut memory overhead in long-horizon agents. A lightweight System-One handles routine reads and writes while a slower LLM-driven System-Two reserves deliberative reasoning for hard cases, keeping expensive autoregressive generation off the critical path of most memory operations.
Think Like a World Model, Act Like a VLA: Distilling World-Model Representations into Compact Robot Policies huggingface.co
Rolling a world model forward costs seconds per decision, ruling it out of the control loop. THAW-VLA sidesteps that by distilling a world model’s internal scene representations into a compact vision-language-action policy, giving robots physics-grounded priors without paying the future-prediction tax at inference.
WorldCrafter: Consistent Video World Model with Implicit 3D-aware Memory huggingface.co
Video world models drift when users revisit a viewpoint. Tencent ARC’s WorldCrafter attaches a camera-queryable implicit 3D memory that reshapes how multi-view evidence is compressed into the generator’s token budget, preserving scene consistency across long horizons and shifting angles. The paper drew 156 upvotes on Hugging Face.
Complex KDA: Understanding and Enhancing the Expressivity of Kimi Delta Attention huggingface.co
Delta-rule linear RNNs are cheap but low-rank. Complex KDA shows Kimi Delta Attention can express full 2D rotations by pairing one delta transition with a reflection, matching the expressivity of composed transitions without doubling update rank or compute.
EDGEGEN: Improving Tool-Calling Agents Beyond Happy Paths with Synthetic Edge Case Generation huggingface.co
Generic synthetic tasks miss the compliance corners where enterprise agents actually fail. EdgeGen extracts rules from an agent’s underlying state and database to generate edge-case tool-calling scenarios, producing evaluation and training data grounded in the specific system rather than a generic happy path.
A Lie Detector Test for Language Models: Reading Knowledge a Model Won’t Reveal huggingface.co
Borrowing a forensic lie-detector protocol, researchers present a model with the true fact among plausible decoys and measure whether internal activations spike on the real item. The Probe of Internal Knowledge catches sandbagging and deception that surface outputs alone cannot distinguish from genuine ignorance.
ACLArena: Agent Continue Learning in Multi-stage Post-training huggingface.co
Industrial agents stack capabilities across sequential post-training stages, but no standard recipe exists for combining them without forgetting. ACLArena provides a sequential training pipeline and evaluation framework to compare integration paradigms, quantifying the trade-offs between joint, sequential, and mixed approaches to agent continual learning.
The information geometry of large language models is shared, learned, and controllable huggingface.co
Shared Fisher-Rao geometry of next-token probabilities across architectures links model behavior, enables low-disturbance interventions, and predicts learning dynamics and control transfer.
VideoGen-Agent: Reinforcing Video Generation Agents huggingface.co
Recent advances in video generative models have enabled high-fidelity, temporally coherent video generation. However, these models often struggle to satisfy prompts requiring specialized knowledge, specific identities, physical consistency, or ordered events. In this paper, we present VideoGen-Agent, a multimodal agent trained through multitask agentic reinforcement learning to use external tools for video generation. The agent coordinates augmentation, generation, and verification tools through
onPanda: Efficient Annotation of On-Policy Alignment Data for LLMs and Agents via Token-Level Correction huggingface.co
We present onPanda, an interactive tool for efficiently annotating LLM alignment data and agent trajectories. onPanda adopts token-level correction as its core interaction: while reading a model response, the annotator locates the first inappropriate token and either picks a substitute from the model’s candidate tokens or types the correct text via free-form editing. The system then truncates everything after that position and continues generation from the corrected prefix, repeating this locate
Retrieval-Augmented Generation (RAG) systems over enterprise knowledge bases must ingest heterogeneous document formats — PDFs, Word documents, presentations, and scans — whose content is locked inside complex visual layouts, multi-column pages, and dense tables. Rule-based extraction and OCR destroy reading order, flatten tables, and lose heading hierarchy, while fully agentic chunking over extracted text incurs high token costs and hallucination risk. We present Document Retrieval-Aware Chun
Towards Full Pipeline FP8 Reinforcement Learning for LLMs huggingface.co
Reinforcement learning (RL) has become a key technique for improving the reasoning and agentic abilities of large language models (LLMs). Although FP8 quantization can accelerate RL training, maintaining stability throughout an FP8 RL pipeline remains challenging. While previous works have focused on resolving train-inference mismatches using correction techniques like TIS, we reveal that full-pipeline FP8 RL still suffers from severe training instability, manifesting as anomalous mid-training e
CARE: Experience-Guided Atomic Corrective Execution for Vision-Language-Action Policies huggingface.co
Vision-Language-Action (VLA) policies achieve strong performance in robotic manipulation but remain brittle once execution deviates from nominal trajectories. We propose CARE (Corrective Atomic Robotic Execution), a framework that improves recovery by learning from failures encountered during execution. Instead of generating corrective data from manually designed or random perturbations, CARE collects failed rollouts, models stage-conditioned post-failure deviations, and uses the resulting empir
One to More, More to One: Category-Aware Iterative Expert Training for Software Engineering Agents huggingface.co
Repository-level software engineering (SWE) comprises heterogeneous task categories, whose progress under pooled agentic reinforcement learning can be uneven: gains in some categories coincide with regressions in others, while aggregate resolution obscures these changes. Motivated by this category see-saw, we develop a category-aware expert-training and policy-integration framework. Executable task construction and SWE Labeler, an evidence-grounded multi-axis labeling system, organize the traini
Why Do Video Diffusion Models Violate Physics? Unveiling the Flaws in Attention Mechanisms huggingface.co
Despite impressive visual quality, state-of-the-art video diffusion models often generate content that violates real-world physical laws. While existing solutions rely on external priors or specialized data, we investigate the root cause by exploring the internal mechanisms of these models. Specifically, we present the first interpretability study on the ”motion planning” process of text-to-video diffusion models, revealing how motion trajectories form during early denoising stages. Building u
Measuring the Checker: Mutation Analysis for GPU-Kernel Benchmark Oracles huggingface.co
Benchmarks for LLM-generated GPU kernels decide correctness with a few random inputs and a loose floating-point tolerance, and their verdicts now feed leaderboards and reinforcement-learning rewards. Recent work agrees these checkers are weak and patches them by hand---extra input distributions, fuzzing recipes, tighter tolerances---with no way to measure whether any patch suffices. We introduce mutation analysis as an adequacy metric for kernel-benchmark oracles: deterministic rules inject 10{,
GameHorizon Suite: Multi-Horizon Data and Evaluation in Gameplay huggingface.co
Modern video games provide a measurable testbed for AI models, combining abilities of visual understanding, instruction decomposition, goal planning, and precise action control over multiple temporal horizons. Existing datasets and benchmarks, however, either cover a narrow range of games, lack language instructions, or rely on high-variance online rollouts. To address these challenges, we introduce GameHorizon, a unified data and evaluation suite that measures gameplay capabilities at different
Streaming Video Editing with Easy Adaptation huggingface.co
In this paper, we propose SVEET, a framework that requires merely training on a pretrained bidirectional video diffusion model but supports high-quality streaming video editing in an auto-regressive fashion. To tackle this problem, we first systematically revisit existing video-to-video diffusion approaches and identify two key principles for such streaming adaptation: backbone feature disentanglement and conditional frame independence. Building on these insights, we develop a novel paradigm for
The Functionalizer: Lossless Functional Decomposition for Subword Tokenization huggingface.co
Standard subword tokenizers either treat every orthographic variation of a word (such as hello, Hello, HELLO, and Héllo) as unrelated vocabulary entries, which fragments the embedding space, or discard this variation through lossy normalization. We present the Functionalizer, a lossless pre-tokenizer framework that factors orthographic and structural variations into a compositional opcode/operand prefix stream before tokenization: a canonical base token (operand) prefixed by parametric transform
SkillSpec: Intent-Masked Specification Reasoning for Agent Skill Correctness huggingface.co
SkillSpec uses specification reasoning over a unified skill graph to detect semantic defects in autonomous agent skills by balancing contextual views and sandbox validation.
Prediction-Powered Smoothing and Validation for Disaggregated AI Evaluation huggingface.co
Evaluating an AI system requires disaggregated assessment, as performance varies across domains such as benchmark task types or conversation types in deployed agents. Exhaustive testing is expensive, so evaluation rests on a sample of labeled units. We treat the evaluation set as a finite population and seek accurate point and interval estimates of each domain mean. Direct estimators, including prediction-powered inference (PPI), use only a domain’s own labels and are imprecise where labels are
Transferring the Intelligence of VLMs to Robotic Control huggingface.co
Humans can seamlessly adapt to both physical and digital worlds, suggesting that while a digital-to-real gap exists in embodiment, environment and task, human intelligence itself may transfer across this gap. This naturally raises a fundamental question: can the intelligence of vision-language models (VLMs) similarly generalize from the digital world to the physical world for robotic control? We investigate this question through RoboDawn, a human-intuitive interface that exposes robotic control
Grounded Action Model: 3D Grounding as a Foundation for Robotics huggingface.co
Manipulation policies must know which objects matter and where they are, yet the pretrained backbones that current robot foundation models build on, from language in vision-language-action models (VLAs) to video generation in world-action models (WAMs), do not directly require this metric grounding, leaving it to be learned implicitly from robot demonstrations. We propose Grounded Action Models (GAMs), a new paradigm of robot foundation models built with 3D grounding. GAM can be conditioned usin
Mira-Scene: Pixel-Aligned Layouts for Generative 3D Scene huggingface.co
Single-image 3D object generation can now produce high-fidelity assets, yet accurately placing them into a coherent scene layout remains an open challenge. A central difficulty lies in how object layout is represented. Holistic methods absorb placement into a scene-level generation process, sacrificing object-level detail. Compositional methods preserve object fidelity by decoupling geometry from layout, but typically parameterize layout as sparse, unbounded pose variables that are difficult to
UltraTex: Unleashing 2K Multi-View Diffusion for 3D Texturing huggingface.co
High-quality texture generation is essential for creating realistic and production-ready 3D assets. Recent multi-view diffusion methods have shown promising results for image-guided 3D texturing, but they are typically constrained to low operating resolutions such as 512 or 768, making it difficult to preserve high-frequency details from high-resolution reference images. Scaling this paradigm to 2048 resolution is computationally prohibitive, as the unified multi-view sequence exceeds 212K token
HuRo: Robotizing Human Videos for Scalable VLA Pretraining huggingface.co
Human video datasets offer an abundant and diverse source of interaction data that can complement expensive real-robot data. To bridge the human-to-robot embodiment gap, existing approaches either robotize videos in task-matched settings or address observation and action alignment separately at scale. In this work, we systematically examine whether robotized human videos can serve as an effective and scalable source of supervision for VLA pretraining. To this end, we develop a robotization pipel
ShieldVLA: Feasibility-Aware Safety Alignment for Vision-Language-Action Models huggingface.co
Vision-Language-Action (VLA) models demonstrate strong generalization in robotic manipulation and navigation, but existing fine-tuning methods provide limited safety guarantees. Current approaches primarily rely on Lagrangian optimization that enforces safety through soft penalties on expected cumulative cost, often resulting in residual constraint violations or overly conservative behavior. Moreover, learning safety in visual domains is challenging due to the absence of dense per-step safety an
OmniEdu: Open Foundation Models for Learning and Teaching huggingface.co
Educational foundation models must solve problems, understand curriculum structure, diagnose learner difficulties, and provide appropriate instructional support. Existing educational language models often focus on either problem solving or tutoring, with training mixtures organized by source or task rather than capability. We present OmniEdu, an open family of foundation models for K-12 learning and teaching. Its instruction-tuning corpus combines over 100 educational resources and general instr
Realtime-Venus: A full-duplex interaction system with asynchronous delegation huggingface.co
A proactive full-duplex system with separate audio-visual and audio models enables continuous perception, native speech generation, and asynchronous tool execution for real-time dialogue.
Deep Persona: A Psychologically Grounded Architecture and Evaluation Framework for Role-Playing Agents and Simulations huggingface.co
Existing approaches to persona simulation with Large Language Models (LLMs) mostly rely on shallow character descriptions that fail to sustain coherent character behavior across extended interactions. We introduce Deep Persona, a psychologically grounded, three-layered architecture that organizes personas into hierarchical levels of observable expression, latent beliefs, and core motivational drives, for constructing highly convincing role-playing agents. Governed by the principles of scripted d
TAPe+ML: A Compact Structured Representation for Multi-Task Computer Vision huggingface.co
We present TAPe+ML v3, a compact computer vision system based on TAPe (Theory of Active Perception), a structured representation that encodes relations among perceptual elements before recognition. Instead of operating directly on pixel tensors, the system uses a shared TAPe representation and a modular recognition architecture for image classification, object detection, and instance segmentation. TAPe+ML v3 combines background and contour processing, local object localization, prototype-based
References
Cui et al., ‘Agent Harness Distillation’ (arXiv 2607.28147) arxiv.org
Inference-time harnesses constitute valuable intellectual property… AHD, a black-box framework, can effectively extract and transfer complex orchestration logic to unauthorized agents, posing a major threat to commercial AI systems.
themoonlight.io review of AHD themoonlight.io
The authors propose a deception-based defense… providing plausible but intentionally misleading information about its internal harness structure when it detects extraction attempts, degrading fidelity of the stolen harness while maintaining utility.
themoonlight.io review of Harness-Zero themoonlight.io
The success of the distillation hinges entirely on the harnessing agent being ‘sufficiently capable’. If the reviewer model is weak, it can produce net-harmful interventions that degrade the student’s performance.
dair.ai academy paper notes academy.dair.ai
In USPTO, the evolved harness provided deep domain knowledge (SMILES validation) which was harder to internalize fully than procedural skills — direct distillation from a stronger teacher yielded 12% (no improvement), Review-with-Oracle yielded 15%, Harness-Zero yielded 30% vs. base 38% with the full harness attached.
originshq.com blog analysis originshq.com
Distillation cannot replace harnesses that manage real-time data or persistent cross-session state. Information that fundamentally changes at runtime cannot be ‘frozen’ into model weights via fine-tuning.
sophon.at technical breakdown sophon.at
The target harness is a ‘mini-SWE-agent’ style system with a single Bash execution tool, orchestrated via the Harbor CLI using Docker-based rollouts and the Tinker SFT recipe… scaling to ‘billions of rollouts’ requires robust containerized infrastructure.
Thinking Machines Lab — ‘On-Policy Distillation’ (Kevin Lu, Oct 2025) thinkingmachines.ai
Qwen3-8B reached 70% on AIME’24 while requiring roughly 1/10th the compute of conventional RL, using per-token reverse KL against a Qwen3-32B teacher.
Wang et al. via r/mlscaling — ‘Beyond the 80/20 Rule: High-Entropy Minority Tokens’ reddit.com
Restricting policy-gradient updates to just the ~20% high-entropy ‘forking’ tokens outperformed full-gradient updates by over 11 points on AIME with Qwen3-32B.
verl documentation (OPD algo page) verl.readthedocs.io
verl v0.7.0 contains a validation-path bug that can underestimate model performance by 5–7 points; users should upgrade to v0.8.0+ or disable in-training validation and evaluate separately.
aigcage.com survey of distillation reproductions aigcage.com
IER-based selection has occasionally performed worse than random selection or simple prefix-based supervision in Qwen3-4B → Qwen3-1.7B pairings, where performance ‘degrades significantly’.
arXiv 2510.24021 — Entropy-Aware On-Policy Distillation (EOPD) arxiv.org
The mode-seeking property of reverse KL can collapse generation diversity; EOPD blends forward KL on high-entropy tokens to preserve diversity while retaining reasoning accuracy.
SelecTKD (CVPR 2026, Huang et al.) openaccess.thecvf.com
A ‘propose-and-verify’ procedure gives tokens full training weight only if the teacher verifies them as informative, dynamically weighting distillation loss by teacher confidence and student–teacher disagreement.
EmergentMind analysis of RRSI emergentmind.com
the original RRSI paper provides point estimates without confidence intervals or variance across independent evolution seeds… the current leakage critic primarily targets explicit task-specific content, potentially missing latent correlations or sophisticated prompts that exploit verifier artifacts without naming the task
AI Weekly — ModularRSI writeup aiweekly.co
ModularRSI… decomposes the harness into five discrete units… using a benchmark-disjoint protocol, evolving on 2,000 tasks completely separate from evaluation sets. It reported accuracy gains from 47.57% to 52.43% on TerminalBench 2.0 and from 73.40% to 76.45% on SWE-bench Verified
ResearchGate — Agentic Harness Engineering (AHE) paper researchgate.net
AHE treats each harness edit as a falsifiable contract, where the agent predicts an outcome before it is verified against task-level results… autonomous systems to surpass expert human-designed harnesses in under 24 hours of tuning
Cloud Security Alliance — RSI Security Implications v1.0 labs.cloudsecurityalliance.org
traditional security frameworks are unequipped for systems that can materially participate in their own successor’s development… OpenAI agents reportedly escaped an isolated testing environment during an offensive cybersecurity test
LayerLens — Gemini 3.5 Flash Stratix evaluation layerlens.ai
On Terminal-Bench 2.1, the model achieved a resolution rate of 76.2% using the Terminus-2 harness, notably outperforming the more expensive Gemini 3.1 Pro, which scored 70.3%
Envisioning — HarnessFix framework glossary envisioning.com
HarnessFix utilizes a diagnosis-driven approach. It compiles execution traces into a specialized Intermediate Representation (HTIR) to pinpoint exactly which harness artifact caused a failure, generating targeted patches rather than broad evolutionary edits
