Sources

Proof of concept for watermarking AI-generated proteins while preserving biological function.

StudentBench: AI and human tutoring yield equivalent GRE learning gains huggingface.co

Artificial intelligence offers an unprecedented opportunity to augment human capabilities, yet progress at the frontier has focused primarily on advancing model capabilities. We introduce StudentBench, a suite of AI teaching evaluations and a public platform that enables large-scale data collection with over 175,000 student-AI messages to study whether large language models (LLMs) produce learning gains equivalent to human tutoring. Using StudentBench, we measured learning gains on Quantitative

Hunyuan-A13B Technical Report huggingface.co

Hunyuan-A13B is an open-source Mixture-of-Experts model with 80B total parameters that routes only 13B per token, cutting inference cost while preserving capability. Pretraining ran on a 20T-token corpus with heavy STEM curation, followed by supervised fine-tuning and large-scale reinforcement learning.

Schrödinger’s Code Repository: Have LLMs Learned SWE-bench or Memorized It? huggingface.co

Repository-level coding benchmarks reuse popular open-source projects that appear in training data, so strong scores may reflect memorization rather than reasoning. SchrodingerRepo dynamically instantiates fresh repositories to probe whether agents actually reason about code or recall canonical cues.

The Linear Representation Hypothesis Needs a Group Action huggingface.co

The Linear Representation Hypothesis is usually stated without defining when two representations count as equivalent, so metrics, probes, and interventions quietly test different claims. The authors argue the hypothesis should be reformulated around an explicit group action that fixes which structure is preserved.

Capable yet Parsimonious: Extracting and Characterizing Hidden Chain-of-Thought in Frontier Models huggingface.co

Registering a dummy tool through standard API features coaxes closed frontier models into writing out intermediate reasoning that providers normally hide. The authors validate the extracted traces against native chain-of-thought on open models to check whether they reflect real reasoning or post-hoc rationalization.

EmbodiedSWE: Coding Agents for Long Horizon Dexterous Robotics huggingface.co

EmbodiedSWE-Bench is a simulation suite covering contact-rich manipulation, deformable objects, and jobs needing up to 30 minutes of continuous interaction. Frontier coding agents solve many long-horizon tasks and transfer solutions across them, suggesting a scalable supervision source for general robot policies.

Self-Organizing Agent Teams Learn to Reason Together huggingface.co

Most multi-agent systems rely on hand-coded routing or task decomposition, which breaks when solution structure is unknown. Self-Organizing Agent Teams instead let fixed groups of LLM agents discover roles and divisions of labor from experience, mirroring how human teams adapt as a problem unfolds.

Calibration as a First-Class Criterion in LLM Evaluation huggingface.co

Methods for measuring whether a model’s confidence matches its accuracy already exist, but new models, datasets, and benchmarks routinely ship without reporting them. The authors frame this adoption gap as a core obstacle to trustworthy evaluation, since miscalibration quietly undermines downstream decisions.

WhatWorkedBench: Benchmarking Experimental Understanding in AI Agents huggingface.co

AI research agents need reliable knowledge of how their experiments change outcomes. We introduce WhatWorkedBench to measure experimental understanding, the accuracy of predictions about component changes after budgeted experimentation. Agents inspect code, select measurements, and submit a response surface, a table predicting scores for every configuration of component settings. Exhaustive CPU execution supplies reference effects for changing each component while holding the others fixed. These

InternW0: A Foundational Physical World Model for Efficient Real-World Interactions huggingface.co

Physical intelligence requires more than predicting how the world may evolve: predictions must remain actionable as the world continues to change. We introduce InternW0, the first instantiation of the InternW physical world model series from Shanghai AI Laboratory, built around omnimodal interfaces, asynchronous multi-frequency processing, and local physical modeling under partial observations and external influences. InternW0 jointly learns future visual dynamics and continuous robot control th

Uranus: Building the Next-Generation Simulation Infrastructure for Embodied AI huggingface.co

Scalable simulation is essential for robot data generation, policy training, evaluation, and safe iteration, yet real-world interaction is costly and conventional simulators require labor-intensive construction. We present Uranus, a data-driven robot simulator built around a joint-trajectory-conditioned autoregressive diffusion model. Uranus offers three key capabilities: (1) streaming, open-ended rollout, which receives future joint-position trajectories online and autoregressively generates on

FLEET: From Logits Entropy to Enhanced Trajectories in Text Generation huggingface.co

Solutions based on large language models (LLMs) often rely on temperature sampling to improve accuracy and stability by aggregating multiple samples from the completion distribution. However, this memoryless approach is inherently suboptimal: because it lacks awareness of prior generations and their evaluations, it produces an increasing proportion of semantically duplicate answers as more samples are drawn, leading to diminishing returns. To address this limitation, we introduce FLEET, a novel

Verifiable Hidden Dynamics Play: Generating Agentic RL Environments from Solved Mechanisms huggingface.co

Language-model agents increasingly face long-horizon tasks with evolving state, interdependent decisions, and delayed outcomes. Scaling their training requires diverse agentic environments, dependable outcome signals, and low extension cost. Existing generation pipelines commonly construct an environment before defining its outcome rule or annotating its trajectories, leaving dynamics and evaluation to be aligned post hoc. VHD-Play reverses this dependency by sampling and solving a mathematical

HappyWorld-Bench huggingface.co

Evaluating world models requires assessing both the quality of the worlds they generate and their consistency and responsiveness under exploration, interaction, and modification. We introduce HappyWorld-Bench, a comprehensive benchmark that evaluates whether generated worlds remain reliable as agents interact with them. Our design is built on a hierarchical capability framework of six world capabilities (W1-W6), from generative construction to unified world modeling, instantiated across three in

X-Planner: Event-Structured Task Planning for Embodied Intelligence huggingface.co

Task planning bridges high-level instructions and executable behavior in long-horizon manipulation, yet modern Vision-Language-Action (VLA) systems often leave this intermediate structure implicit. Existing chain-of-thought (CoT) planners also tend to rely on coarse task-level annotations or serialize long reasoning traces token by token. We present X-Planner, a planning front-end that addresses both the supervision and representation of embodied reasoning. Our planning data combine Ego, UMI, an

PACT: From Credit Assignment to Critic Alignment huggingface.co

Reinforcement learning has become a central component of large language model (LLM) post-training, yet token-level credit lacks a generally accepted mathematical definition, leaving its relationship to commonly used training signals unclear. We formulate three regularity conditions, namely Completeness, Prefix Consistency, and Neutrality, and prove that they uniquely determine token-level credit. This characterization provides a unified basis for explaining phenomena across existing algorithms a

The Past Frames the Future: Memory for Autoregressive Video Generation huggingface.co

Advances in generative models have improved video fidelity, enabling long-horizon generation, interactive world modeling, and evolving visual environments. Autoregressive (AR) video generation extends visual sequences through causal rollouts. However, a fundamental bottleneck emerges: as the generated sequence expands, practical models must operate under strictly bounded context windows, storage, and computational limits. Consequently, critical historical information, e.g., entity identities, dy

Spatial-Interactor: Learning Spatial Reasoning through Interaction with the Observable Physical World huggingface.co

Spatial reasoning is essential for vision-language models (VLMs) to understand and act in the physical world. Reasoning in dynamic environments requires VLMs to perceive local state transitions caused by object motion and viewpoint changes and integrate them over long trajectories to maintain an updated spatial state, yet existing VLMs remain limited in both capabilities. Current spatial training primarily focuses on static questions about object attributes and spatial relations, providing limit

Knowledge Pull Requests for Continual Document Authoring huggingface.co

We introduce Knowledge Pull Requests (KPRs), a framework for continual document authoring that makes each change interpretable. Documents require ongoing revision as new knowledge surfaces from other sources, languages, or times, but existing approaches either edit with no account of what knowledge changed or regenerate from scratch. A KPR integrates new knowledge into a document by extracting claims, filtering and routing them to sections, and flagging conflicts with existing content, producing

On the Diffusibility of High-Dimensional Latents huggingface.co

Representation Autoencoders (RAEs) enable diffusion models to operate in the feature spaces of pretrained visual encoders. However, many off-the-shelf encoders are not optimized for faithful reconstruction, discarding fine-grained visual details. As expected, finetuning these encoders for image reconstruction recovers such details. However, perhaps counterintuitively, this procedure reduces the effective dimensionality of the resulting representation, and the altered geometry has downstream effe

GeoPair: Geometry-Preserving Cross-Layer Factorization for Training-Free Transformer Compression huggingface.co

Transformer architectures exhibit cross-layer redundancies, yet post-training compression pipelines typically optimize layers in isolation or rely on heuristic grouping strategies that disregard layer-specific activation geometries. We introduce a principled, training-free framework that sequentially optimizes cross-layer weight pairings and shared-dictionary factorizations. Rather than forcing weights of adjacent layers to share a basis or heuristically merging activation statistics, our approa

PackLab: A Comprehensive Framework for Developing, Training, and Evaluating MLLMs in Robotic Bin Packing huggingface.co

Robotic bin packing requires long-horizon sequential decision-making, as each object placement affects the available space for subsequent packing. Existing methods primarily rely on hand-crafted geometric heuristics that optimize predefined objectives or reinforcement learning policies learned through trial and error over predefined training configurations. Despite recent advances in multimodal large language models (MLLMs) for this task, their potential for closed-loop sequential decisions acro

MemBodied: Recurrent Associative Memory for Vision-Language-Action Models huggingface.co

Vision-Language-Action models provide a strong foundation for general-purpose robot control, yet a vast majority of policies do not preserve and leverage episode-level information beyond the current observation. This limitation is consequential in history-dependent manipulation tasks that depend on information available only in past observations. Retaining past observations in context can aid in recovering this information, but at the significant cost of ever-growing, bloated context and inferen

All modalities are equal, but video is more equal: Closing the Cross-Attention Gap in Joint Video Generation huggingface.co

Video is a rich representation of a physical event, capturing appearance, geometry, motion, and temporal evolution. Other modalities, such as 3D body motion or audio, encode narrower aspects of the same event. We find that joint multimodal diffusion transformers exhibit a corresponding asymmetry in cross-modal correspondence: companion modalities develop strong correspondences to video, but the reciprocal correspondences through which they constrain video remain substantially weaker. We express

RewardVerse: Rubric-Guided Policy Optimization for Video Reward Modeling huggingface.co

Reinforcement learning (RL) is vital for optimizing video generation models, with a robust reward model (RM) serving as the cornerstone. However, existing video reward models often produce unstable scalar scores because they directly map complex, subjective video quality into a single score without explicit evaluation criteria. This leads to scalar drift, where the scoring scale collapses or shifts across different prompts, making the reward unreliable for RL. Drawing inspiration from profession

MemoryAthena: Adaptive Routing over Latent and Generated Memories huggingface.co

Learned-memory methods store information in an explicit table and consume it through a separate reader, allowing addressing, storage, and reading to be modified independently. We study whether useful memory can also be generated rather than only retrieved. MemoryAthena uses three pathways: direct Engram retrieval (E), generation from retrieved Engram cues (GE), and generation from causal backbone states without consulting the memory table (GH). Generated memory is conditionally useful: it can co

Just-in-Time Memory: Learning to Curate Task-Adaptive Memory for LLM Agents huggingface.co

Agentic memory systems reuse past experience to improve future performance, yet most existing designs curate memory at write time: once a task is completed, its trajectory is distilled into a fixed artifact, such as a reflection, workflow, skill, or reasoning strategy, that is later retrieved by similarity. This forces the system to decide what is worth remembering before the future query is known, irreversibly discarding information and producing a query-independent summary that must serve many

SpeakerMem-R1: Speaker-Centered Dual-Track Memory for Multi-Party Dialogue huggingface.co

Long-term conversational memory in multi-party settings requires more than retrieving relevant content from long-term conversations: it must distinguish who said what, whom each statement concerns, how individuals perceive one another, what information is shared by the group, and how states change over time. Recent studies on multi-party dialogue benchmarks show that existing general-purpose LLM memory systems tend to lose person and group relations or struggle to integrate clues distributed acr

Six Layers Less: Encoder Pruning for Whisper with Label-Free Recovery huggingface.co

Pruning large pre-trained transformer-based ASR models such as OpenAI’s Whisper has seen great adoption, as pruning the decoder led to significant end-to-end transcription speedups. For instance, the {\tt whisper-large-v3-turbo} variant reduced the decoder from 32 to 4 layers, while Distill-Whisper similarly reduced the decoder to only 2 layers. Although some attention has been put towards reducing the size of the encoder, no approach has seen wide adoption. This could be due to the need for cus

References

Medium (Predict) — on Acemoglu’s response medium.com

A Nobel Economist called the Anthropic CEO’s AI job warning ‘motivated reasoning’ and ‘self-serving nonsense.’

Reddit r/artificial discussion of the study reddit.com

A robot might flip a pancake but fail to clean a spill, necessitating a human handler whose cost negates the robot’s efficiency — the index overlooks failure recovery and coordination costs.

MarketScale / PMMI (packaging industry trade group) marketscale.com

Packaging robots are now cheaper than labor over the long run… high labor costs and worker shortages have moved robotics from a discretionary expense to a primary business justification.

Gizmodo on Tesla Optimus gizmodo.com

The complex assembly of robot hands, involving over 100 manual parts per unit, remains a major bottleneck; Gen 3 hands have 22 degrees of freedom yet remain fragile and difficult to mass-produce.

Goldman Sachs humanoid market forecast goldmansachs.com

The market could reach $38 billion by 2035, with shipments rising to 6.5 million units as manufacturing costs drop by 40% annually.

O*NET Center AI Impact Review onetcenter.org

O*NET’s Detailed Work Activities are discrete units that do not represent the messiness of real-world workflows; the 50% time-reduction threshold is arbitrary and obscures the spectrum of augmentation versus automation.

FoldMark GitHub (Zaixi Zhang et al.) github.com

FoldMark… claims over 95% bit accuracy at 32 bits, allowing tracking of up to 1 million unique users… tested on EGFP (98% of wildtype fluorescence) and CRISPR-Cas13 (95% efficiency), with code and an interactive Hugging Face demo released.

Microsoft Research (Horvitz/Wittmann, Science 2025) news.microsoft.com

The team generated 76,089 AI-‘paraphrased’ variants of 72 proteins of concern including ricin; before patches, up to 100% bypassed commercial screening, and even after patches ~3% of the most hazardous variants still evaded detection.

Unite.AI analysis of SynthID Bio unite.ai

The watermark is designed at the amino acid level so it survives codon optimization, but remains vulnerable to ‘protein paraphrasing’ and adversarial substitutions; DeepMind acknowledges robustness against deliberate removal is still an open problem.

Science Media Centre expert reaction (Simon Clarke, Reading) sciencemediacentre.org

The team excluded human pathogens from training data, but the capability to design ‘fitter’ viruses is now widely accessible, and not all future researchers may show similar restraint.

AI Weekly summary of SynthID Bio Nature paper aiweekly.co

ProteinMPNN sequences achieved 100% true-positive detection at 0.1% false-positive rate; AlphaFold 3 structural watermarks held 99.8% detectability at the same FPR.

NTI ‘Risky Business’ policy brief nti.org

An open letter signed by the CEOs of OpenAI, DeepMind, Anthropic and Microsoft calls for mandatory screening of all DNA synthesis orders; benchtop synthesis devices without hardware-level controls remain a major bypass risk.

Chalkbeat on Khanmigo Tennessee RCT chalkbeat.org

Access to Khanmigo raised math achievement by approximately 0.06 to 0.08 standard deviations over a school year… the median student messaged it on only a third of their practice days, and lead researcher Philip Oreopoulos characterized the AI as a ‘non-event’ because students ‘rarely engaged it in substantive mathematical dialogue.’

Harvard Gazette on Kestin PS2 Pal study news.harvard.edu

A custom AI tutor produced learning gains roughly twice as large as instructor-led active-learning sessions, with effect sizes of 0.7 to 1.3 standard deviations and students reaching mastery in 49 minutes versus 60–75 minutes in class.

Upstack.ai review of AI vs human tutoring literature upstack.ai

Human tutoring reliably yields an average effect size of ~0.79 σ while older intelligent tutoring systems averaged 0.66 σ; cost per standard deviation of improvement runs $13,000–$19,500 for human tutors versus $1,200–$2,000 for AI subscription platforms.

TutorAI meta-analysis summary (2026) tutorai.me

When AI tutoring programs scale beyond 1,000 students in public schools, average effect size often shrinks to 0.14–0.21, whereas high-dosage human tutoring reliably maintains roughly 0.29 SD.

Moonlight review of StudentBench themoonlight.io

The no-tutoring control watched unrelated educational videos; critics argue the realistic counterfactual is independent self-study with consumer AI like ChatGPT, and the one-hour post-test cannot speak to durable learning or long-term retention.

Curtis Northcutt personal site curtisnorthcutt.com

Northcutt pioneered ‘confident learning’ at MIT, founded Cleanlab (acquired by Handshake in early 2026), and now directs Handshake AI Research — the same group funding and publishing StudentBench.

Sources

References

Jack Sun, writing.