Sources

MiMo-V2.6: Scaling Reinforcement Learning Towards Self-Improvement huggingface.co

The MiMo-V2.6 series advances omni-modal foundation models through scaled reinforcement learning with larger batches, diverse agent environments, and robust reward grading, supported by stable training infrastructure.

Memento 3: Model-Based Recursive Self-Improvement through Reflective Rulebooks huggingface.co

Memento 3 enables frozen LLM agents to continuously learn explicit, revisable world models via external memory and verified code compilation, achieving perfect performance on ARC-AGI-3 and autonomous Atari Pong mastery.

Scaling to Tens of Thousands of Test-Time Iterations with Loop-Native Attention Residuals huggingface.co

InfiLoop introduces a loop-native residual mechanism that selectively retains past computations and suppresses unreliable updates, enabling recursive Transformers to scale reasoning depth without degradation.

TestPrism: Rethinking Test Evaluation Beyond a Single Reference huggingface.co

Grading LLM-generated unit tests against one reference overstates reliability, TestPrism finds. Companion system TestHelix lifts test quality through heterogeneous synthesis, peer cross-validation, and recursive self-improvement, offering coding agents a sturdier signal than the Joint Success Function alone.

A Closer Look at Agentic BBO: Benchmarking LLM Agents for Black-Box Optimization huggingface.co

Across hyperparameter tuning, database tuning, chip design, and molecular design, agentic LLM pipelines push the Pareto frontier past direct LLM prompting and classical numerical baselines. The benchmark isolates how optimization tools, task semantics, and base model choice drive the gains.

Embodied Turing Machines: Stateful Code for Robot Recursive Self-Improvement huggingface.co

Swapping runtime vision-language calls for explicit state-tracking code, the Embodied Turing Machine framework speeds robot control, recovers from failures, and enables recursive self-improvement. Authors report gains on bimanual tasks where a VLA-plus-harness stack typically stalls.

Can AI Agents Make Open-Ended Scientific Discovery? Evidence from Station huggingface.co

In an open-world multi-agent sandbox called Station, LLM researchers equipped with a supervisor and meta-reflection loop rederive most criteria from recent papers and surface novel findings, pushing evidence that agentic systems can run open-ended scientific discovery without a fixed task.

What Did the Agent Actually Do? Evidence-Grounded Oversight for Long-Horizon Agents huggingface.co

A training-free method called EBG builds an evidence-grounded behavior graph that flags an agent’s consequential decisions and links each to supporting traces. Paired with AgentMonBench, it gives overseers a way to verify autonomous executions without retraining the agent.

Accurate but Not Humble: Evaluating Epistemic Humility in LLM Agents under Knowledge Conflict huggingface.co

Trajectory-level analysis shows agents often spot conflicts between parametric memory and retrieved evidence, yet still answer confidently without flagging doubt. The study warns that high task accuracy masks poor epistemic humility, and tests model-level interventions to close the gap.

SparseEngine: Sparse-First Inference Engine huggingface.co

Built sparse-first rather than retrofitted, SparseEngine wraps diverse sparse-attention schemes behind shared KV-cache state management, Chain Cache, and Prefix-Cache Pruning controls. The result is higher decoding throughput for long-context LLM agents without rewriting each method’s kernels.

Post-Training Frontier Text-to-Image Models by Composing Preference and Rubric Rewards huggingface.co

Post-training text-to-image models with composed preference and rubric-based rewards improves human preference ratings and robustness to reward hacking.

Do LLMs Understand Sequential Structure? A Controlled Study of Inference and Generation huggingface.co

Large language models struggle to recover hidden sequential rules in interactive and continuation tasks, with longer context and correct recognition failing to ensure faithful simulation of conditional dependencies.

Mara Chain: Rethinking Failure as a Stepping Stone for AI System Auto-Evolution huggingface.co

Mara Chain improves optimization of AI artifacts by refining rejected candidates into stepping stones, reducing rollouts and boosting performance across multiple benchmarks.

Opera: A Verbal Critic Framework for Long-horizon Coding Agents huggingface.co

Opera is a persistent verbal critic framework that tracks feedback resolution over time, improving long-horizon coding agent performance and generating effective training data.

TokenRouter: Efficient Serving System for Token-Level LLM Routing huggingface.co

TokenRouter is a serving system for token-level routed LLM inference that improves throughput by using request-centric programming with asynchronous subservers and optimized delayed-batching schedulers.

ReSPO: Reshaped Sequence Policy Optimization for Gradient Starvation in Off-Policy Learning huggingface.co

ReSPO replaces clipped policy optimization with a smooth sequence-level kernel to prevent gradient starvation and improve off-policy reinforcement learning with reused rollouts.

Learn2Play Bench: How Well Do LLM Agents Learn from Experience in Unfamiliar Environments? huggingface.co

Learn2Play Bench evaluates LLM agents on novel text-based games to measure learning from interaction rather than pretrained reasoning, revealing benefits of full experience retention, a human performance gap, and harness design effects.

MC-Sparse: Deconstructing and Closing the Dense-Sparse Attention Gap in Diffusion Transformers huggingface.co

Meta-Cached Sparse Attention improves diffusion transformer efficiency by selecting individual key-value tokens and reusing cached metadata across denoising steps, achieving faster generation with minimal quality loss.

V-CoLA: Vision Token Compression with Linear Attention huggingface.co

V-CoLA is a training-free vision token compression framework for linear attention that preserves model performance while significantly reducing computational overhead.

SparseDecoding: Decoding-Aware Pruning for Accurate and Efficient LLM Inference huggingface.co

SparseDecoding improves LLM decoding by aligning pruning calibration with autoregressive generation and optimizing sparse matrix-vector kernels for faster inference.

U-Space: Uncovering When and Why Uncertainty Arises in Language Models huggingface.co

Researchers introduce U-Space, a low-dimensional subspace that measures and interprets evolving model uncertainty via token-level maps without requiring labels, repeated sampling, or training.

REMORY: Learning Residual Memory for Context Compaction huggingface.co

REMORY enhances long-horizon agent memory by adding learnable soft tokens to textual summaries, improving decision accuracy with minimal context usage.

Retrieval-Centric Deep Learning in Growing Nonparametric Neural Networks huggingface.co

Kernelized attention layers with functional gradient-based rules establish a retrieval-centric deep learning paradigm that improves learning efficiency and connects to advanced neural network optimizers.

AgentGarten: Code Worlds for Evolving Agents huggingface.co

AgentGarten couples simulators with a shared neural renderer to build real-time interactive virtual worlds, enabling agents to learn efficiently through inherited experience and adversarially distilled visual observations.

From Traces to Agentic Worlds: Agentic Language World Models for Interactive Environment Simulation huggingface.co

Trace2Env enables realistic environment simulation for LLM agents by reconstructing historical traces into a reusable worldbook, improving fidelity and long-horizon consistency without rebuilding original systems.

SpatialOPSD: Self-Distilling Spatial Intelligence from Verified Coding Agent Traces huggingface.co

SpatialOPSD internalizes spatial reasoning into multimodal language models via self-distillation of verified agent traces, eliminating external tool dependencies while improving accuracy and generalization.

SanSi: A Looped Typed Decision Model for System 1.5 Thinking huggingface.co

SanSi converts looped language models into typed decision models that iteratively refine hidden states without token generation, improving accuracy and extending solvable reasoning depth across diverse tasks.

OneSearch-VL: Unified Multimodal Deep Research Agent for Image and Video huggingface.co

OneSearch-VL is a unified visual research agent using a Visually Grounded Evidence Graph to coordinate grounding, retrieval, and reasoning across images and video, with specialized training data, a traceability reward, and new benchmarks demonstrating large gains over prior models.

A GPU-Parallel Framework for Heterogeneous Multi-Task Reinforcement Learning huggingface.co

Hebero is a GPU-parallel benchmark for heterogeneous robot manipulation that enables multi-task reinforcement learning, and its DGPO framework with IW-ABC improves success by adaptively using demonstrations and weighting lagging tasks.

SpaceCast-Bench: Evaluating Predictive Spatial Reasoning in Vision-Language Models huggingface.co

SpaceCast-Bench evaluates predictive spatial reasoning through observation, transformation, and inference tasks, revealing large gaps between models and humans and showing that fine-tuning improves generalization.

ViSkill: Reinforcing VLM Agents with Evolving Visual-Native Skills huggingface.co

ViSkill is a visual-native framework that encodes successful agent interactions as reusable visual skill cards to jointly improve policy optimization and skill accumulation through a closed feedback loop.

BrickBench: Evaluating Agentic Brick Design huggingface.co

BrickBench evaluates agents on text-conditioned LEGO design requiring physical validity and semantic alignment, revealing that current agents meet basic constraints but lag behind human quality.

SuperNav: An Agentic Navigation System for Any Task in Any Scene huggingface.co

SuperNav equips a pretrained multimodal large language model with a specialized agent harness and navigation tools to perform general-purpose robot navigation without fine-tuning, using a visual-point interface to link decisions to motion.

Reasoning-Informed Visual Editing huggingface.co

RISEBench++ benchmarks reasoning-driven visual editing across hierarchical reasoning dimensions and introduces a training-free agentic framework to improve instruction following, consistency, and plausibility.

Chaos in the Text: Revealing the Modality Preference in Mixed-Modality Retrievers huggingface.co

Trident improves mixed-modality retrieval by balancing text, image, and fused views to reduce text bias and modality sensitivity.

USDCraft: Geometrically Grounded Programmatic Modeling of Articulated 3D Assets for Simulation huggingface.co

USDCraft uses a pretrained LLM to generate executable programs that build simulation-ready articulated 3D assets from partial geometry, enabling real-to-sim robot manipulation.

Foundations of Large Language Models huggingface.co

This is a book about large language models. As indicated by the title, it primarily focuses on foundational concepts rather than comprehensive coverage of all cutting-edge technologies. The book is structured into six main chapters, each exploring a key area: pre-training, generative models, prompting, alignment, inference, and reasoning. It is intended for college students, professionals, and practitioners in natural language processing and related fields, and can serve as a reference for anyon

WorldGuide: Goal-Directed Video World Model for Procedural Task Execution huggingface.co

WorldGuide couples planning and execution in a closed-loop visual framework to generate procedural videos that adapt to prior outputs, using hierarchical memory and a new step-level benchmark.

DreamTrue: Action-Faithful Robot World Model with Counterfactual Post-Training huggingface.co

DreamTrue is a multi-view robot world model that improves action-faithful video prediction through geometric calibration, counterfactual post-training, and a learned video reward model, achieving state-of-the-art results on AgiBot.

Multi-Agent Egocentric World Model with Fine-Grained Embodied Interaction huggingface.co

ME-World synchronizes first-person video generation for multiple interacting agents by jointly denoising shared token sequences with cross-view action conditioning and shared environment memory.

One Block, Multiple Depths: Recurrent Vision Transformers with Depth-Programmed Experts huggingface.co

Recurrent single-block vision transformers with depth-conditioned expert mixtures match full-depth encoder accuracy at reduced storage and enable elastic-depth deployment.

OmniCapBench: A Deep-Structured Evaluation Framework for Fine-Grained Audio-Visual Captioning huggingface.co

OmniCapBench evaluates multimodal language models on audio-visual reasoning by scoring atomic, verifiable units across entities, shots, and audio events to expose temporal and cross-modal failures.

In-context Robot Learning Made Simple: A Democratized Recipe for Manipulation Tasks huggingface.co

Robotic in-context learning is formally defined and implemented via a minimal framework that enables robots to learn tasks from visual demonstrations without large-scale pretraining.

VibeEdit: Image Editing with Canvas Instructions huggingface.co

VibeEdit enables precise text-guided image editing via spatial annotations and structured instructions, improving target selection and local edit quality through layer-decoupled conditioning and reinforcement learning.

From Prompting to Composing: A Spatial Canvas Interface for Poster Generation huggingface.co

A spatial canvas interface with semantic, identity, text, and pixel bindings enables direct compositional poster generation via an adapted image editing model, supporting both manual and automated planning with scalable training and improved controllability.

Beyond Spatio-Temporal Priors: A Generalizable Approach for Dense Correspondence Matching huggingface.co

FreeMatching establishes identity-preserving dense correspondence across non-physical transformations by combining generative and semantic representations with heterogeneous supervision and iterative refinement.

OuroWorld: Bringing Any 3D World Alive as Diverse, Endlessly Looping 3D Cinemagraphs huggingface.co

OuroWorld converts static 3D Gaussian Splatting scenes into looping dynamic 3D cinemagraphs with diverse motion via a vision-language guided video synthesis pipeline and an inconsistency-robust periodic 4D deformation model.

SpaceFlow: Locally Controllable 3D Generation huggingface.co

SpaceFlow enables local geometric and appearance control in 3D generation by assigning per-primitive control levels and routing part-specific text or image cues during synthesis.

LEGO: A Lifting-Free Approach for Exocentric-to-Egocentric Video Generation huggingface.co

A learned view synthesizer provides structural alignment for a video diffusion model to generate egocentric video from exocentric recordings without explicit depth or point clouds, improving novel view synthesis.

Pumpire: Unified Benchmark for Metric Distance Estimation huggingface.co

Pumpire is a benchmark that directly evaluates point-to-point distance estimation of 3D foundation models using a large annotated dataset and unified protocol.

References

Vals AI audit (YouTube walkthrough) youtube.com

approximately 67% of the coding environments (1,795 out of 2,698) [had] the solution or ‘fix’ commit still readable within the local Git history… deleted fixes survived as unreachable Git objects [retrievable] using git fsck —unreachable or by inspecting the reflog

shattered.io — MiMo-V2.6 Pro / Claude probe shattered.io

Anthropic has alleged that Xiaomi may have used ‘illicit model distillation’ from Claude to train its systems, though Xiaomi has not officially addressed these claims

r/LocalLLaMA thread on MiMo-Distill-Qwen-9B reddit.com

the default chat template ‘crippled’ the model’s reasoning capabilities in popular runtimes like llama.cpp… the original template failed to correctly handle tool calls and reasoning boundaries, leading to a swift community-led fix

MindStudio — MiMo livestream recap mindstudio.ai

a live ‘cost counter’ showed the MiMo-V2.6-Pro and Flash models consuming over $1.13 million in compute within the first 48 hours… the logs also exposed seven full restarts due to VRAM infrastructure errors and dataset detection failures

Muown paper (arXiv 2607.20548) arxiv.org

Muown treats the row-magnitude vector as an explicit variable… stabilizes the spectral norm and expands the range of effective learning rates. Independent benchmarks on dense retrievers found that… Muon-class optimizers fit training data better [but] did not necessarily improve out-of-distribution generalization compared to AdamW

Kingy AI benchmark review kingy.ai

MiMo-V2.6 Pro… passed [23 automated coding and data checks] perfectly at a cost of roughly $0.03—compared to over $1.00 for Claude Opus 5

ARC Prize Foundation (arcprize.org) arcprize.org

The 25-task public set was intentionally designed to be fully solvable by humans as a ‘front door’ for developers; a 100% score on it is expected and does not signify that ARC-AGI-3 is ‘solved’ — the official leaderboard remains focused on the private evaluation set.

machine.net — independent writeup of Memento 3 machine.net

Memento 3 reached its 21-0 Pong shutout after roughly 9,504 emulator frames, versus ~400,000 for EfficientZero V2 — a claimed 42× sample-efficiency gain — but the comparison involves only three seeds against a fixed opponent.

rewirebio.io — ‘Code World Models: Teaching LLMs to Simulate Execution’ rewirebio.io

Code-as-World-Model approaches decouple reasoning from visual/linguistic prediction by generating Python modules as simulators, avoiding the ‘semantic inertia’ where distributional priors override contextual logic — a lineage Memento 3 sits inside rather than invents.

arXiv 2508.16153 — original Memento (M-MDP) paper arxiv.org

The earlier Memento framework introduced a Memory-augmented MDP with a soft-Q case-selection policy and hit 87.88% Pass@3 on GAIA validation — establishing the ‘learn without weight updates’ thesis that Memento 3 now extends from tool-use to world-model synthesis.

pelles.ai — ‘Recursive Self-Improvement: State of the Evidence’ pelles.ai

Current bounded self-refinement loops polish existing skills by ~20–34%, but closed-loop systems without new external signals eventually hit information entropy; open-ended RSI likely requires architectural discontinuity that frozen weights cannot deliver.

Sakana AI — Multi-Agent Self-Supervision (MASS) sakana.ai

When a model acts as both student and teacher in a homogeneous loop, it risks reinforcing its own blind spots and reward-hacking its own evaluation criteria — a failure mode Memento 3’s single-LLM ‘rulebook fidelity’ check does not obviously escape.

machinesdoitbetter.ai (TRM explainer) machinesdoitbetter.ai

TRM achieved 44.6% accuracy on ARC-AGI-1 and 7.8% on ARC-AGI-2 … surpassing DeepSeek-R1, Gemini 2.5 Pro, and o3-mini on these specific datasets.

GitHub: ShlokaTomar/TRM-Embeddings-Experiment github.com

Replacing a learned task ID with a blank or never-seen identifier can cause performance to collapse, with cell accuracy dropping by over 60%.

rekursiv.ai blog (ARC-AGI Pareto record) rekursiv.ai

rekursiv.ai set a state-of-the-art Pareto record by achieving 17.5% on ARC-AGI-2 … with inference costs between 16 and 11,600 times lower than its accuracy peers.

AI Weekly summary of DeepLoop (Princeton/UCLA, Jul 2026) aiweekly.co

While standard transformers use a scaling exponent of 1/4, looped models require this to increase to 1/2 as the loop count grows … enabling stable training for models that revisit blocks.

TechTimes on Meta ‘Loop Scaling Laws’ (Oct 2026) techtimes.com

The compute-optimal loop count is often lower than researchers initially hoped, with many production systems settling on just two to three loops to balance reasoning power with inference speed.

paulkuo.tw — Looped transformers & monitorability paulkuo.tw

Recurrent reasoning can hide the model’s internal logic, making ‘hidden reasoning’ traces harder to interpret than explicit Chain-of-Thought tokens.

Sources

References

Jack Sun, writing.