Reasoning-trace replay leaks 62 keys, AMIE runs video visits, LLaDA hits Qwen3
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
AMIE promotional video
Stealing Reasoning Traces from Proprietary LLM APIs simonwillison.net
Stealing Reasoning Traces from Proprietary LLM APIs A vanity domain name ( stolen-thoughts.com ) for a neat paper : Anthropic, OpenAI, and Google return encrypted chain-of-thought blocks to clients that can be replayed across sessions, users, and models. We take a trace produced by a frontier model, replay it into a weaker sibling, jailbreak the weaker model, and recover the stronger model’s hidden reasoning in plaintext You can see an example of these encrypted blocks by running: curl https://…
LLaDA MoE v2: Scaling Mixture-of-Experts Diffusion Language Models huggingface.co
Researchers establish scaling laws and design principles for mixture-of-experts diffusion language models, then train a 30B-parameter model that achieves competitive performance with fewer pretraining tokens.
Hunyuan3D-Buffalo 1.0: A Unified Multimodal Model for Scalable 3D Generation, Understanding, and Editing huggingface.co
Hunyuan3D-Buffalo 1.0 folds text-to-3D generation, instruction-guided editing, part generation and 3D understanding into one multimodal architecture trained on a large 3D corpus. Tencent pairs a Hunyuan3D-VLM with a diffusion transformer and reports leading benchmark results across all four tasks.
MiniWorld: Democratizing the Training of Video World Models from Scratch huggingface.co
MiniWorld trains autoregressive video world models from scratch using a block-causal diffusion transformer with flow matching. A chunk-wise noise schedule and rolling KV cache enable pipelined asynchronous denoising for streaming inference, aimed at making embodied-AI world models reproducible outside frontier labs.
JoyAI-Video-Edit: Real-Time Open-Ended Video Editing with Autoregressive Diffusion huggingface.co
JoyAI-Video-Edit is a 16B-parameter autoregressive diffusion framework for streaming video editing on a single GPU. Source-Anchored Distribution Matching Distillation and long-horizon autoregressive distillation cut train-inference mismatch and temporal drift, preserving source fidelity across long clips.
When Attention Goes Blind: Numerical Failure in ALiBi Positional Encodings huggingface.co
ALiBi positional encoding pushes distant-token attention weights below float precision, zeroing them and breaking passkey and needle-in-a-haystack retrieval in decoder models. Swapping in log-scaled distances restores retrieval, though default ALiBi slopes remain a strong baseline on shorter contexts.
Search, Inspect, Fetch: Exploiting Boolean Retrieval for Deep-Research Agents huggingface.co
SIEVE replaces vector-only retrieval with a fielded Boolean query language, letting agents filter and rank documents through structured result cards before selectively fetching sections. The approach lifts answer accuracy on deep-research tasks while cutting token usage across retriever and backbone choices.
When Many Answers Are Valid, Voting Fails: Symbolic Verification for Best-of-K Causal Reasoning in LLMs huggingface.co
CALVER scores best-of-K reasoning traces against Pearl’s causal criteria — d-separation, backdoor adjustment, intervention — to pick valid answers without references. The training-free symbolic verifier outperforms plurality voting and larger LLM judges on causal tasks where many answers look plausible.
AURORA-LM: Autoencoding Unified Representation for Continuous-Latent Diffusion Language Modeling huggingface.co
AURORA-LM generates text through a block-causal diffusion transformer over continuous latents rather than discrete tokens, using flow matching with self-trajectory consistency and noise-level calibration. A query-based encoder-decoder keeps latents decodable at high capacity, positioning diffusion as a viable alternative to autoregressive LMs.
SkillJack: Persistent Skill Backdoors in Self-Evolving Agents huggingface.co
Self-evolving agents can be compromised when poisoned interaction histories are converted into persistent malicious skills through their own learning pipeline, reducing detectability and surviving source deletion.
MerchantBench: Benchmarking LLM Agents for Long-Term Coherence in E-Commerce Operations huggingface.co
MerchantBench evaluates long-term coherence of LLM agents in a year-long e-commerce simulation requiring persistent decision-making across sourcing, pricing, and cash-flow management with mixed feedback delays.
Video-DeepResearch: Towards the Next-Generation Multimodal Deepresearch Agent huggingface.co
Video-DeepResearch extends multimodal agents to continuous video via decoupled perception-exploration and GRPO training, achieving state-of-the-art results on a new video reasoning benchmark.
Quo Vadis, World Modeling? huggingface.co
This work proposes agent-centric interactive world proxies that provide diverse feedback for continual agent improvement across inference, training, and co-evolution stages.
PAST-Bench: Benchmarking the Foundations of Recursive Self-Improvement in Personal Agents huggingface.co
PAST-Bench evaluates whether persistent AI agents improve from retained experience across tasks, and Hermes+ enhances this via targeted loop interventions.
When Agents Learn to Be You: Benchmarking Privacy Leakage, Impersonation Risk, and Defenses in Persona Skills huggingface.co
AntiSkillBench evaluates privacy and authenticity risks in distilling persona interaction histories into reusable agent skills, revealing persistent vulnerabilities and limited defense effectiveness.
Know When to Stop: Segment-Level Credit Assignment for Reducing Overthinking huggingface.co
DASH improves reasoning model accuracy by shaping rewards based on whether reasoning segments progress toward or away from correct answers, reducing unproductive self-reflection.
ReflectRL: Learning from Golden Negative Trajectories via Reflective-to-Direct Reasoning huggingface.co
ReflectRL improves reasoning in large language models by learning from failed expert trajectories through reflective reasoning and policy transition during on-policy training.
PCSD: Persistent Consistency for Self-Distillation in Agentic Reinforcement Learning huggingface.co
Persistent Consistency Self-Distillation improves reinforcement learning for language model agents by weighting token-level teacher guidance according to sustained local support, yielding stronger results on interactive tasks.
TurnSight: Turn-Level Hindsight Self-Distillation for Tool-Integrated Reasoning huggingface.co
TurnSight improves reinforcement learning for tool-integrated reasoning by using turn-level hindsight self-distillation with cross-horizon agreement to refine credit assignment.
OmniPack: Unified Token Compression for Efficient Omni-modal Large Language Models huggingface.co
OmniPack is a training-free framework that combines structural pre-LLM token compression with query-guided semantic refinement inside the LLM to reduce computational overhead in omni-modal models while preserving performance.
Any-OPD: Heterogeneous On-Policy Distillation for Flow-Matching Models via Representation-Space Bridging huggingface.co
Any-OPD enables cross-family on-policy distillation of latent flow-matching generators by comparing outputs in a frozen vision representation, aligning continuous noise levels, and anchoring via the student VAE.
CAPEval: A Decoupled Caption Evaluation across Understanding and Generation huggingface.co
CAPEval separates caption quality into coverage and precision, revealing that coverage predicts understanding performance while precision predicts generation performance.
GROVE: Growing and Reasoning over Temporally Stratified Memory from Streaming Video Experience huggingface.co
GROVE is a training-free framework that unifies reactive video question answering and proactive assistance through a single causally growing memory with multi-scale retrieval.
ContinualSkillBench: Can LLM Agents Truly Evolve Their Capabilities? huggingface.co
The study introduces a benchmark to evaluate whether language model agents can continuously learn and reuse skills across tasks, finding that adaptation improves performance but robust skill consolidation remains limited.
Are the Financial Reasoning from LLMs Credible? A Real World Test over Long-Horizon Statements huggingface.co
FinIndices reveals that large language models suffer from knowledge and structural bottlenecks in financial reasoning, which can be partially mitigated by supervised fine-tuning.
ExplainBench: Evaluating Code Explanations from Agents huggingface.co
ExplainBench evaluates coding-agent explanations by testing whether they help LLMs answer questions about buggy code and patches, revealing frequent inaccuracies and showing that an audit agent can improve trustworthiness.
RestoreKV: Recovering Full-Cache Behavior Under Aggressive Query-Agnostic KV Cache Eviction huggingface.co
RestoreKV improves query-agnostic KV cache eviction by learning to restore evicted information via lightweight adapters, reducing accuracy loss under tight budgets without altering base eviction rules.
ST-WAM: Semantic-Temporal World Action Model for Robust Manipulation under Visual Distribution Shifts huggingface.co
ST-WAM improves robot action robustness by combining DINOv3 semantic features with VAE dynamics via dual-space future experts and current-anchored retrieval, reducing sensitivity to visual distribution shifts.
ARCHead: Activation-Metric Residual Correction for Large Language Model Output Heads huggingface.co
ARCHead compresses the language-modeling head of large transformers via a quantized low-rank core and group-wise INT4 residuals with an activation-derived correction, cutting storage by nearly 4x with minimal perplexity impact.
Better, Stronger, Faster, and Broader: Structured All-Mask Prediction for MLLM-Based Segmentation huggingface.co
STAMPlus resolves the segmentation trilemma by decoupling dialogue from non-autoregressive multi-target mask prediction, achieving high performance, preserved multimodal ability, and faster inference.
Knowledge-Geometry Decoupling: Refreshable Pretrained Transfer for Streaming Recommendation huggingface.co
Knowledge-Geometry Decoupling separates behavioral pretraining from task-specific geometry via multi-token prediction and orthogonal residual adaptation, improving recommender transfer under continual refresh.
UniWorld-Design: From Pixel Generation to Layer-Native Design huggingface.co
UniWorld-Design introduces layer-native generative models that produce structured semantic RGBA layers for compositional image creation, understanding, and instruction-driven editing.
Decoding Children’s Gait Behavior huggingface.co
A new dataset and framework enable automated fine-grained analysis of children’s gait from standard video to support clinical assessment of developmental disorders.
Push-Wiper: Toward General-Purpose Robotic Cleaning across Varied Stains and Surfaces with Segmented Pushing Trajectories huggingface.co
Push-Wiper reformulates viscous stain cleaning as aggregation using adaptive pushing sequences generated by Diffusion Policy, achieving high removal rates and zero-shot generalization across stain types and surfaces.
PosterMELD: Multi-Agent Paper-to-Poster Generation for Controllable Design Diversity with Editable Print-Ready Outputs huggingface.co
PosterMELD is a multi-agent pipeline that generates editable scientific posters with high print-ready rates, design controls, and low cost via template-conditioned generation and bounded repair.
LegalPincite: Multi-level Legal Information Retrieval Dataset huggingface.co
A large-scale legal retrieval dataset built from CJEU judgments provides masked queries, full paragraph corpora, and expert-validated citations for rigorous case- and paragraph-level evaluation.
ChronoLens: Measuring Language Change Across Time, Languages, and Linguistic Levels huggingface.co
A framework using sparse crosscoder representations of multilingual parliamentary texts reveals that linguistic levels change in comparable magnitudes but diverge across languages and time in direction and timing.
CURV: Enhancing Chart Understanding Through Curriculum Visual Grounded Reasoning huggingface.co
CURV is a curriculum learning framework that trains multimodal models to perform step-by-step visual reasoning with dynamic spatial grounding for chart question answering.
Multi-Task Multi-Frame Visual Piano Transcription huggingface.co
V2N is a visual piano transcription system that jointly predicts note onsets, offsets, key holds, and velocities using multi-task supervision and extended temporal context to improve accuracy.
References
AI Governance News aigovernance.com
Researchers recovered 367 PII artifacts and 182 credentials — including 62 live API keys — from 315,320 encrypted reasoning blocks scraped from public agent logs and GitHub repositories.
AI Weekly — ‘Encrypted Reasoning Cracked’ aiweekly.co
Johns Hopkins cryptographer Matthew Green reported the replayable-blob behavior to OpenAI and Anthropic in May 2026; both bug-bounty teams initially responded that they ‘did not see security implications in side channels or replays’.
AI Weekly — Kimi K3 distillation debate aiweekly.co
Some analysts pointed to Kimi K3’s reasoning-token cadence and self-identification as ‘Claude’ as circumstantial evidence that trace-extraction attacks may already have been used in the wild — a claim Moonshot and independent researchers like Nathan Lambert dispute given the two-week release gap.
CybersecurityNews cybersecuritynews.com
The root cause is a provider-wide global encryption key rather than per-session binding; a full architectural fix requires session-level cryptographic isolation that none of the three providers has publicly confirmed shipping.
AI Security Portal aisecurity-portal.org
Because models treat their own prior reasoning as authoritative, an attacker can craft a malicious trace, encrypt it into a portable block, and drop it into public workflows — victim agents then perform ‘silent egress’ of data with no visible instruction in the chat history.
r/LocalLLM discussion reddit.com
Commenters argued the flaw is ‘security by obscurity’ and that as long as reasoning is stored client-side to save provider compute, some permeability is inevitable — proposed mitigations (per-turn keys, server-side session store) all raise infrastructure costs the labs have so far refused to eat.
Google Research blog — BIDMC feasibility study research.google
AMIE’s differential diagnosis included the correct final diagnosis in 90% of cases… physician supervisors did not need to intervene for safety once during the trial, though doctors still outperformed AMIE on the practicality and cost-effectiveness of management plans.
Science Media Centre — expert reaction to AMIE and MIRA sciencemediacentre.org
AMIE remains a closed system, making it impossible for external scientists to verify the results or check for potential data contamination where training data might have overlapped with the study’s test cases.
Microsoft Research — Sequential Diagnosis with Language Models (MAI-DxO) microsoft.com
MAI-DxO achieved an 85.5% accuracy rate on the Sequential Diagnosis Benchmark (SDBench) using 304 NEJM cases, versus roughly 20% for experienced physicians without external aids, while ordering 20% fewer tests.
Harrison PLLC Substack — ‘What happens when doctors supervise’ harrisonpllc.substack.com
The ‘clinician-in-the-loop’ model may shift a disproportionate burden of safety onto physicians… reviewing ‘black box’ algorithmic outputs can be mentally taxing, and the system might propose costly, unavailable, or impractical next steps that create more work for the human supervisor.
Medium (Kairi AI) — ‘The decision clinical AI can’t own’ medium.com
AI’s tendency to provide comprehensive, risk-averse management plans could lead to over-diagnosis and unnecessary emergency room crowding… clinical trust is built on accountability, which a model cannot yet assume.
News-Medical — AMIE disease-management study news-medical.net
Specialist physicians rated AMIE’s treatment recommendations as appropriate in 87% to 94% of cases across successive visits, whereas participating human physicians scored between 62% and 71%.
CMU ML Blog — ‘Diffusion Beats Autoregressive in Data-Constrained Settings’ blog.ml.cmu.edu
DLMs benefit from an ‘implicit data augmentation’ effect… showing a much higher ‘half-life’ for data reuse—approximately 500 epochs compared to 15 for AR models.
AI Weekly — ‘Ant Group’s LLaDA MoE v2 Nears Qwen3 on 65% of the Tokens’ aiweekly.co
Critics argue that the term ‘approaches’ is intentionally vague and may obscure significant deficiencies in tasks where diffusion models have historically struggled… initial reports failed to provide detailed per-benchmark scores or clarify whether the SFT recipes were fairly matched to Qwen3’s sophisticated post-training pipeline.
aimodels.fyi — LLaDA2.0-flash vs mini comparison aimodels.fyi
Ant Group also released the dInfer framework, which claims to be up to 10 times faster than competing frameworks like Nvidia’s Fast-dLLM, enabling the v2 models to generate over 1,000 tokens per second on HumanEval.
arXiv 2507.15857 — DIJA jailbreak framework for masked diffusion LMs arxiv.org
The bidirectional nature of MDLMs allows for ‘interleaved mask-text prompts’ that force the model into harmful completions… this ‘priming vulnerability’ can achieve nearly 100% attack success rates on some models, as parallel decoding limits the ability of traditional safety guardrails to filter content dynamically.
Chris Hughes (Medium) — ‘Beyond Vanilla MoE: Fine-Grained and Shared Experts’ medium.com
DeepSeek-V3 activates only ~5.5% of its total capacity (37B active out of 671B)… newer Qwen3 variants like the 235B model activate ~9.4%. The 33% activated capacity benchmark is often cited in the context of ‘dense-like’ MoEs where higher activation provides better stability at the cost of higher VRAM and compute requirements.
Vector Labs — ‘Diffusion Language Models Are Getting Fast Enough to Matter’ vector-labs.ai
While lab results show 6x gains, real-world measurements on hardware like DGX Spark often settle closer to 1.75x to 2x faster, frequently requiring quantization to reach peak efficiency… MDLMs do not support traditional KV caching, requiring vanilla multi-head attention.
