Sources

LongStraw: Long-Context RL Beyond 2M Tokens under a Fixed GPU Budget huggingface.co

A growing gap separates inference context lengths from RL post-training: inference systems are approaching million-token contexts, while post-training workloads often remain at 256K tokens or below and rely on length generalization at deployment. The gap is especially important for AI agents, whose observations, tool outputs, documents, and prior decisions accumulate over long trajectories. LongStraw is an architecture-aware execution stack for million-token RL post-training under a fixed GPU bu

RoboTTT: Context Scaling for Robot Policies huggingface.co

Recent robot foundation models operate with single-step or short-history visuomotor context. We introduce Test-Time-Training Robot Policies (RoboTTT), a robot model and training recipe that scale visuomotor context to 8K timesteps, three orders of magnitude beyond state-of-the-art policies, without growing inference latency. At this context length, we unlock new robot capabilities: one-shot in-context imitation from human video demonstrations, on-the-fly policy improvement, robustness to perturb

Smarter and Cheaper at Once: Byte-Exact KV-Cache Grafting Turns a Frozen Small Model into a Verified-Knowledge Flywheel huggingface.co

We report a way to make a frozen small language model both more capable and dramatically cheaper at once, without changing any weights. Verified knowledge is deposited once as a byte-exact key-value (KV) state artifact and later restored, by graft, into a fresh inference context. The restore is bit-exact: under a pinned deterministic configuration, the grafted logits are byte-for-byte identical to a fresh computation (SHA-256 equality), with zero KL divergence and 100% argmax agreement over fift

Video = World + Event Stream huggingface.co

Wan-Streamer v0.3 organizes native-streaming video generation around a two-part view: a persistent world holding scene, subjects, and acoustic conditions, and an event stream carrying everything that changes over time. The split targets interactive generation where environments stay stable while behavior, speech, and scene changes evolve.

WanSong v1.0 Technical Report huggingface.co

WanSong ditches autoregressive and multi-stage pipelines in favor of a single diffusion model that directly generates commercial-grade, long-form audio with controllability. The technical report frames the approach as a simpler path to high-fidelity song synthesis without the AR-then-diffusion stacking common in music foundation models.

On Locality and Length Generalization in Visual Reasoning huggingface.co

A study probes whether sequential, foveated vision — closer to how humans glimpse scenes — offers computational advantages over global one-shot models. The paper argues locality helps visual reasoning generalize to longer, harder inputs, a regime where standard ViTs that ingest whole images at once degrade.

RxBrain: Embodied Cognition Foundation Model with Joint Language-Visual Reasoning and Imagination huggingface.co

Hy-Embodied-RxBrain represents embodied plans as a single sequence interleaving language reasoning and visual imagination, rather than separating scene understanding from future prediction. Tencent Hunyuan released the 1.0 model and code, positioning it as a foundation model for robots that must connect task-level reasoning to physical states.

BadWAM: When World-Action Models Dream Right but Act Wrong huggingface.co

BadWAM attacks the assumption that coupling action generation with world prediction makes embodied agents safer. The framework demonstrates world-action models whose imagined futures look benign while executed actions misbehave, breaking the interpretability check that lets operators verify a robot’s plan against its predicted rollout.

Demystifying On-Policy Distillation: Roles, Pathologies, and Regulations huggingface.co

A systematic analysis of on-policy distillation in LLM post-training finds it steers students toward correct reasoning paths through dense token-level guidance but does not raise the capability ceiling. The authors show prompt diversity matters more than per-problem sampling counts, and catalog pathologies plus regularizers.

SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning huggingface.co

SEED tackles sparse trajectory-level rewards in agent training by pairing outcome-based RL with self-evolving on-policy distillation, giving intermediate token-level guidance across multi-turn tool use. The top-upvoted paper of the day on Hugging Face ships code and a project page targeting long-horizon agent optimization.

Spectral Rewiring for Exploration, Purification, and Model Merging huggingface.co

Reinforcement learning has become a standard post-training recipe for large language models, but dense full-parameter updates create two deployment-relevant bottlenecks: suppressed reasoning performance, often reflected by premature saturation of test-time scaling, and interference when consolidating multiple capabilities through multi-domain training or model merging. We show that the reasoning-effective component of these updates is largely concentrated in the base model’s spectral space, moti

From Pixels to States: Rethinking Interactive World Models as Game Engines huggingface.co

Building interactive worlds that respond coherently to player actions has long been a shared goal of computer graphics, games, and artificial intelligence. Recent video generative models provide a data-driven route toward this goal by predicting future observations conditioned on user actions, and are increasingly regarded as potential next-generation game engines. Realizing a genuinely interactive game world, however, requires interaction outcomes that follow rules over evolving game conditions

UniVR: Thinking in Visual Space for Unified Visual Reasoning huggingface.co

Learning broad world knowledge directly from raw visual data is a fundamental capability of intelligence. We introduce UniVR, the first investigation into simultaneously learning complex reasoning, fine-grained physical dynamics, and long-term planning from pure visual demonstrations. At its core, UniVR features VR-GRPO, a reinforcement learning paradigm with complementary global and step-level rewards. This approach enforces logical coherence and physical consistency throughout the reasoning pr

GRASP: GRanularity-Aware Search Policy for Agentic RAG huggingface.co

Agentic retrieval-augmented generation (RAG) extends static RAG by allowing language models to iteratively reason, generate search queries, retrieve evidence, and predict answers. However, it remains challenging for models to decide when to retrieve, whether to use lexical matching or semantic similarity, and how to control context granularity to prevent irrelevant tokens from interfering with agent reasoning. In this paper, we introduce GRASP, a reinforcement learning (RL) framework for trainin

DeepLoop: Depth Scaling for Looped Transformers huggingface.co

Looped Transformers scale sequential computation by applying a compact stack of physical blocks for multiple rounds, increasing unrolled depth without increasing stored parameters. This reuse changes the residual-scaling problem: in an untied Transformer, each residual branch receives and applies its own parameter update, whereas in a looped Transformer one shared update aggregates gradients from repeated visits and is read back by those same visits in the next linearized forward pass. We formal

Rethinking the Evaluation of Harness Evolution for Agents huggingface.co

We revisit the evaluation of automatic harness evolution for LLM agents. Existing harness evolution methods use unit test cases to search for harness configurations and then report final performance on the same public benchmark. This protocol raises two fundamental concerns. First, harness evolution is itself an iterative search procedure that repeatedly evaluates and revises candidate harnesses using task feedback. As in agentic test-time scaling, it should therefore be compared with simple tas

SearchOS-V1: Towards Robust Open-Domain Information-Seeking Agent Collaboration huggingface.co

Recent advances in Tool-Integrated Large Language Models have made web search a core capability of information-seeking agents. However, as interaction histories grow, agents increasingly struggle to track task progress. When search attempts fail to yield useful evidence, current single- and multi-agent systems can become trapped in repetitive loops, wasting search budgets and ultimately compromising the quality and completeness of the final output. We introduce SearchOS, a system-level multi-age

Hierarchical Denoising For Multi-Step Visual Reasoning huggingface.co

Video models are evolving into vision foundation models, yet they still lack human-like multi-step reasoning. Streaming autoregressive diffusion models are efficient but limited in reasoning, while bidirectional diffusion enables global revision with high inference costs due to dense frame-level denoising. Both paradigms struggle to achieve logical consistency and low-latency streaming for complex reasoning tasks. We propose HDR (Hierarchical Denoising for Visual Reasoning), a unified framework

MeanFlowNFT: Bringing Forward-Process RL to Average-Velocity Generators huggingface.co

MeanFlow generators achieve fast few-step sampling by predicting average velocities over time intervals, making them attractive for efficient generation. Reinforcement learning (RL) has become a powerful way to align diffusion and flow models with human preferences and task-specific objectives. In particular, DiffusionNFT offers an efficient forward-process RL framework that does not require reverse-process trajectories or likelihood estimation. However, applying such RL methods to MeanFlow rema

Token Time Continuous Diffusion for Language Modeling huggingface.co

In this paper we introduce token time continuous diffusion (TTCD), a new diffusion language model which (a) operates in continuous space, deterministically mapping Gaussian noise to a final token canvas with no further sampling, and crucially (b) incorporates a new notion of per-token times, with some tokens proceeding from noise to token at a faster rate than others. Continuous space modeling helps TTCD avoid the parallel sampling of multiple tokens, which is a key source of inaccuracy at high

Concurrent Image Understanding and Generation: Self-Correcting Coupled Markov Jump Processes huggingface.co

Human cognition does not separate understanding and generation. A teacher at a whiteboard speaks and draws together, each modality reshapes the other. In this paper, we bring this coupled loop to artificial systems. Masked Diffusion Models (MDMs) are ideally suited to this task, yet existing samplers either decode text and image interleavedly or independently update them in parallel branches that share only previous-step history, but not the other modality’s latest decisions within the same step

Partition, Prompt, Aggregate: Statistical Self-Consistency in Language Models huggingface.co

In-context learning is commonly interpreted as a form of conditional inference, in which the prompt specifies a context and the model’s output is treated as an estimate of the corresponding conditional distribution. If this interpretation holds, then LLM estimates should satisfy basic probabilistic identities. In particular, the law of total probability asserts that prior-weighted conditional distributions aggregate into population-level marginals over any valid partition of the population. In t

VideoChat3: Fully Open Video MLLM for Efficient and Generalist Video Understanding huggingface.co

Recent advances in video understanding have spanned motion, long video, and streaming interaction, driving this field toward real-world applications. Despite this progress, current open-source models remain limited in several ways. They often struggle to generalize across diverse video types, making them effective only in specific domains. High computational demands further restrict their efficiency and scalability. Moreover, most models are only partially open, with key components such as train

VIABench: A Comprehensive Video Benchmark Collected from Blind Individuals for Visual Impairment Assistance huggingface.co

Visually impaired individuals (VIIs) encounter significant daily challenges due to limited access to visual information. Although Multimodal Large Language Models (MLLMs) have achieved impressive results on general vision and language tasks, their practical utility in real-world blind assistance still remains largely underexplored. To fill this gap, we introduce VIABench, a comprehensive video benchmark specifically designed to evaluate MLLMs in Visually Impaired Assistance scenarios using first

KeyFrame-Compass: Towards Comprehensive Evaluation of Keyframe-Conditioned Video Generation huggingface.co

Video generation increasingly relies on keyframe-based workflows, where creators specify a sequence of reference images to guide generation. Although recent models support multi-keyframe conditioning, it remains unclear whether they can faithfully reproduce the prescribed keyframes while maintaining overall video quality. We present KeyFrame-Compass, the first comprehensive benchmark for evaluating keyframe-conditioned video generation. The benchmark contains 386 carefully curated samples spanni

MultiRef-Compass: Towards Comprehensive Evaluation of Multi-Reference-to-Audio-Video Generation huggingface.co

Multi-reference-to-audio-video (MR2AV) generation aims to generate coherent audio-video content conditioned on multiple references and textual instructions. Existing benchmarks mainly focus on text-driven generation, single-reference subject preservation, or isolated audio-video alignment, leaving the emerging MR2AV setting largely unexplored. Compared with these settings, MR2AV requires models to jointly reason over multiple references while generating synchronized visual and audio content. Mod

AsySplat: Efficient Asymmetric 3D Gaussian Splatting for Long-Sequence Scene Modeling huggingface.co

Recent generalizable 3D Gaussian Splatting models have advanced long-sequence novel view synthesis (NVS), but at the cost of substantial redundant computation. We identify that the redundancy can be mitigated based on two observations: (i) high-precision geometry is not strictly required for high-quality NVS; (ii) appearance learning is generally easier than geometry recovery. Motivated by these insights, we propose an asymmetric architecture that decouples geometry and appearance modeling. The

SUFLECA: Scaling Up Feature Learning for CAD-to-image Alignment huggingface.co

CAD-to-image alignment aims to estimate an object’s 9D pose (rotation, translation, and anisotropic scale) from a single RGB image, enabling applications in robotics and augmented reality. Recent zero-shot methods use visual foundation models to match image regions to CAD models, yet typically their correspondences are appearance-driven and degrade under occlusion or sim-to-real domain shift. To address these limitations, we introduce SUFLECA (Scaling Up Feature LEarning for CAD Alignment), a we

Chat2Scenic: An Iterative RAG-Based Framework for Scenario Generation in Autonomous Driving huggingface.co

Validating autonomous driving systems requires diverse, regulation-compliant test scenarios. In simulation-based testing, scenarios are defined as executable scripts. Yet automatically generating such scripts from regulatory descriptions remains an open challenge, and existing approaches face fundamental trade-offs. Retrieval-assemble methods achieve reasonable compilation rates but lack scalability, whereas retrieval-based full-script generation suffers from low compilation success rates. We pr

References

Unsloth blog — Efficient GRPO unsloth.ai

Llama 3.1 (8B) at 20K context requires only 54.3GB of VRAM under optimized GRPO, whereas standard setups demand over 510GB

hyper.ai paper page (STATUS.md summary) beta.hyper.ai

public release is categorized ‘review_only_not_runnable’; full gradient parity shows cosine similarity of only ~0.52 against expected values, effectively failing the correctness contract

presenc.ai — OpenRLHF vs verl 2026 presenc.ai

OpenRLHF generally outperforms verl by 1.2x to 1.7x for medium-scale models… verl’s 3D-HybridEngine eliminates memory redundancy… vital when managing the VRAM pressure of million-token trajectories

r/Qwen_AI thread on Qwen3.6-27B reddit.com

some contributors labeled Qwen3.6-27B an ‘accidental freak,’ questioning if the recipe can be replicated following reported team departures at Alibaba

Michael Hannecke — Hidden Dangers in LLM Frameworks medium.com

the ShadowMQ pattern — unsafe Python ‘pickle’ deserialization — has been found across vLLM, SGLang, and other common backends, potentially allowing attackers to execute arbitrary code on exposed GPU clusters

Exxact — Large Token Context Windows exxactcorp.com

The H20’s high memory capacity (96GB HBM3) makes it effective for memory-bound long-context tasks despite significantly lower TFLOPS than the H100

Corbenic AI product page (Taliesin) corbenic.ai

Taliesin restores AI ‘memory’ across machines and GPU generations via byte-exact KV-state artifacts, with the underlying engine described only at the input-output level.

Layer3Labs — Gemma-4 Explained layer3labs.io

The 31B Dense variant reports 89.2% on AIME 2026 with ‘Thinking Mode’; third-party translation to AIME 2025 puts it around 92.7% — a benchmark community reviewers say is highly sensitive to harness and scaffolding.

arXiv 2605.09990 (CacheBlend-style cached fusion) arxiv.org

CacheBlend selectively recomputes 1–3% of tokens to rectify attention when chunks are reused out of prefix position, cutting TTFT 2.2–3.3x with <1% quality loss — an explicit tradeoff against byte-exactness.

ResearchGate — Schelpe, ‘Byte-Exact Deduplication in RAG’ researchgate.net

Companion paper from the same author formalizing deterministic, lossless deduplication as the substrate the grafting flywheel builds on; benchmarks span RULER, LongBench, HumanEval-Snowball on Gemini 2.5 Flash and GPT-5.1.

Reddit r/ResearchML — Schelpe endorsement request reddit.com

Independent researcher soliciting arXiv cs.LG endorsements on Reddit; commenters warned about seeking academic endorsement via public forums and questioned the practical utility of KV-grafting outside curated reasoning benchmarks.

Ankit Bhattacharjee blog — Prompt engineering & the KV cache ankitbko.github.io

Explicit Anthropic-style cache_control breakpoints and Gemini implicit caching already deliver ~75–90% input-cost discounts and up to 85% TTFT reductions; any early dynamic token (e.g., a timestamp) invalidates the downstream cache.

The Robot Economy — ‘RoboTTT: Fast Weights, Five-Minute Context’ theroboteconomy.news

the ‘strict’ success rate for the five-minute Gear Bot assembly was just 20% (2 of 10 trials)… some practitioners flagged the 79% score as potentially misleading, describing it as a form of ‘benchmark hacking’ where partial credit masks low end-to-end success.

AI Weekly — ‘NVIDIA’s RoboTTT Stretches Robot Context to 8K Timesteps’ aiweekly.co

As of mid-July 2026, NVIDIA had not yet released the RoboTTT code, model weights, or the specific dataset used for the bimanual assembly tasks, raising questions regarding the reproducibility of its ‘one-shot’ imitation capabilities.

alphaXiv — G0.5 vs GR00T N1.7 cross-embodiment evaluation alphaxiv.org

G0.5 reportedly outperformed GR00T N1.7 in bimanual coordination and contact-rich assembly tasks, achieving an average success rate of 76.7% compared to GR00T’s 24.4% in specific cross-embodiment settings.

arXiv 2504.21463 — ‘Test-Time Training Done Right’ (Zhang et al.) arxiv.org

training a 760M parameter TTT-Linear model required 32 A100 GPUs for 95 days, a cost significantly higher than traditional baselines… the high latency of gradient-based inference remains a barrier for real-time deployment.

saurabh.works — TTT layers vs Mamba analysis saurabh.works

while RoboTTT excels at general long-horizon tasks, it may struggle with ‘exact recall’ tasks—often called the ‘needle in a haystack’ problem—where full-attention models still significantly outperform test-time training architectures.

Medium (Zergtant) — TTT sequence modeling primer medium.com

TTT-Linear is faster than Transformers at 8k context and remains competitive with Mamba in wall-clock time… however TTT-MLP faces significant memory I/O bottlenecks due to backpropagation during the inner loop of inference.

Sources

References

Jack Sun, writing.