Sources

WeatherNext: AI model achieves breakthrough in forecasting cyclones deepmind.google

Frontis-MA1: Training an AI4AI Model towards Recursive Self-Improvement in Machine Learning Engineering huggingface.co

Recursive self-improvement (RSI) requires AI systems that improve the process of building AI (i.e., AI4AI); machine learning engineering (MLE) offers a concrete, executable testbed for studying this capability. We introduce OpenMLE, an open full-stack system for RSI research in MLE, spanning verifiable task environments with execution feedback (OpenMLE-Gym), operator learning (OpenMLE-RL), and long-horizon search (OpenMLE-Evo). On this stack we post-train Frontis-MA1 (35B) as a meta-evolution ag

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms huggingface.co

Retrieval-augmented generation (RAG) spans lexical and dense retrieval, graph-based indexing, and agentic search, but these paradigms are usually evaluated on different benchmarks at one corpus size, leaving their accuracy-cost scaling unclear. To bridge this gap, we present a controlled study that varies corpus size along 28 strictly nested tiers spanning roughly 450-fold, while holding questions and a fixed bedrock of relevant and adversarial documents unchanged. Under one reader model and one

Memory Decoder at Scale: A Pretrained, Parametric Long-Term Memory huggingface.co

Memory Decoder at Scale pretrains a dedicated parametric memory module on 300B tokens, decoupling long-term recall from reasoning in decoder-only LLMs. The authors argue standard Faiss retrieval pipelines become infeasible at this data scale, motivating a learned memory of 6.9B parameters instead.

Explorative Modeling: Unlocking a Third Pretraining Axis and End-to-End Generation huggingface.co

Explorative Modeling targets generative modeling’s holdout from the end-to-end training revolution, arguing that factored sampling procedures block joint optimization over multi-modal distributions. The XM framework introduces exploration as a third pretraining axis alongside data and compute, enabling single-stage generative training.

Is Deep Research Reliable? Misleading Knowledge Induces False Conclusions huggingface.co

Deep research agents propagate credible-looking but false web content into their final reports, a new benchmark called MisKnow-Agent finds. The framework injects misleading knowledge into open retrieval environments to test whether planning, synthesis, and report generation stages filter or amplify errors.

Metis: Memory Foundation Model huggingface.co

Metis reframes agent memory as a first-class foundation-model capability rather than an external module bolted onto LLMs. The paper, the day’s top-voted on Hugging Face at 269 upvotes, formalizes memory foundation models alongside multimodal and reasoning models as a distinct axis of native ability.

Echoverse: Deep, Evolving Environments for Training Computer-Use Agents at Scale huggingface.co

Echoverse shifts the bottleneck in computer-use agent training from environment quantity to environment depth, arguing behavioural richness and interaction targeting drive returns. Microsoft’s pipeline generates login-gated, stateful synthetic apps that agents can break and reset, standing in for real production software.

Chimera: Designing and Chinchilla-Scaling Hybrid Visual Diffusion Transformers huggingface.co

Chimera processes text, image, and video tokens in a single raster-ordered stream without positional embeddings, combining Kimi Delta Attention for O(N) long-context tracking with interleaved Multi-head Latent Attention. The authors accompany the architecture with a Chinchilla-style scaling recipe for visual diffusion transformers.

Qwen-UI-Agent Technical Report: Toward Next-Generation Real-World Centric Foundation GUI Agents huggingface.co

Qwen-UI-Agent, topping Hugging Face’s daily list at 302 upvotes, aims for reliable operation on real devices across platforms, blending GUI interaction with CLI execution and long-horizon task completion. The technical report also outlines proactive service initiation and autonomous self-improvement as design goals.

Filesystem-Based Memory for LLM Agents: Organization, Evolution, and Sustainability huggingface.co

Deployed LLM agents increasingly keep their long-term memory as a filesystem: a directory tree of markdown files that the agent itself reads, writes, and reorganizes through generic file tools. Yet research has largely passed over this medium: prior systems design bespoke memory representations and study retrieval over them, leaving the default’s two working assumptions untested: that an agent can keep a growing store organized as memories accumulate, conflict, and go stale, and that this organi

MemHarness: Memory Is Reconstructed, Not Replayed huggingface.co

Retrieving past experiences has become a common strategy to enhance large language model agents. However, most existing memory-augmented agents treat retrieved experiences as static records to be replayed verbatim, injecting them into the context regardless of whether they align with the agent’s current situation. This “replay” paradigm ignores the gap between the abstract, general nature of stored experience and the concrete, ever-changing states encountered at decision time, frequently causi

Multi-Head Attention Residuals huggingface.co

Transformers propagate information across depth through a single additive residual stream: every sublayer reads only the most recent state. Attention residuals relax this by letting each sublayer attend, through a learned softmax. However, that read uses a single query shared across the entire width, so every feature subspace must read the depth history through one distribution. The cost of this forced compromise grows with how much the subspaces disagree about which layers to read, and disagree

Revisiting Lossy Verification in Speculative Decoding: Mechanisms, Trade-offs, and Failure Modes huggingface.co

Speculative Decoding (SD) accelerates large language model inference by allowing a lightweight draft model to propose tokens that are subsequently verified in parallel by a larger target model. Recent approaches introduce lossy verification schemes to further improve efficiency by relaxing strict distributional matching. Yet such relaxation silently rewrites the decoding distribution, and the resulting acceleration can come at the cost of unstable, sometimes severely degraded generation quality.

LEDGERMIND: Provenance-Constrained Multimodal Agentic Reasoning with a Structured Evidence Ledger huggingface.co

Multimodal agents for visual question answering increasingly operate as multi-step trajectories that interleave perception, retrieval, and reasoning, yet evaluation still largely reduces to final-answer accuracy. This aggregate signal cannot tell whether a correct answer was reached through grounded evidence, language priors, or accidental error cancellation. We propose to treat a multimodal agent trajectory as a provenance-constrained state machine: tool outputs are normalized into a Structured

Σ-Mem: An Online Reliability Memory for LLM-based Multi-Agent Systems huggingface.co

Memory is central to long-horizon LLM agents, yet existing memory systems primarily preserve interaction content rather than modeling which agents can be trusted and under what conditions. This limitation is particularly important in multi-agent systems, where a central model may be unable to directly verify plausible or correlated peer responses. We introduce Σ-Mem, an online reliability memory that records historical competence evidence for individual peers and peer relationship evidence acros

Fairness Pruning: Locating Demographic Bias in GLU-MLP Layers via Differential Activations huggingface.co

This work presents Fairness Pruning, a lightweight structural intervention method designed for the management and future mitigation of demographic bias in large language models (LLMs). As a foundational empirical validation of this method, this work focuses on causal bias localization. Using minimally contrastive prompt pairs and inference-time activation capture, the method identifies neurons that react differentially when processing demographic attributes in GLU architectures, evaluating the s

Harness-G: A Graph-Structured Harness for Search Agents huggingface.co

Reinforcement learning (RL) search agents commonly model retrieval as free-form natural-language query generation and optimize multi-turn interactions using final-answer rewards. Current studies mainly improve training with denser or more structured credit signals, but rarely examine whether retrieval is properly formulated at the policy-environment interface. We observe pronounced retrieval aliasing during Search-R1 training: rollouts for the same question continue to generate distinct query st

Beyond Geometric Complementarity: Coherent Overlap in Sparse Mixture-of-Experts Routing huggingface.co

Sparse mixture-of-experts (MoE) language models route each token to multiple experts, suggesting a geometric account of their benefit: co-selected experts should contribute distinct representation directions. Existing evidence often conflates route coherence, candidate quality, and candidate-by-context interaction. We distinguish these quantities using an Expert Subspace Separation Index (ESSI), matched-route residuals, and a prefix-controlled 2times2 factorial; frozen-route interventions and a

SpatialCLI: Learning to Reason With Spatial Tools, Then Without Them huggingface.co

Vision-language models (VLMs) are increasingly used in embodied agents to interpret visual inputs, reason about spatial relationships, and make task-level decisions based on that reasoning. However, a fundamental capability mismatch remains: general VLMs can reason about the overall task but often miss the visual details that determine success, while specialist vision models can capture those details but cannot translate them into task-level decisions. In this work, we propose SpatialCLI, a fram

Beacon: Knowing When and How to Perform Agentic Visual Reasoning huggingface.co

The fundamental goal of agentic visual reasoning is to improve the success rate of multimodal large language models (MLLMs) on complex tasks, rather than merely equipping them with a sophisticated yet inefficient reasoning paradigm. In this work, we rethink agentic visual reasoning through two key dimensions of tool use: Mode Adaptiveness (MA) and Tool Effect (TE). Mode Adaptiveness characterizes whether an MLLM can recognize when tools are truly necessary and invoke them accordingly, thereby av

PhiZero: A World Model Built Around Physical Language huggingface.co

We introduce PhiZero, a physical world model built around physical language, a compact discrete representation of world-state transitions. Existing physical world models typically predict future videos directly in pixel space, leaving the underlying world dynamics implicit within high-dimensional visual predictors. Motivated by humans’ ability to abstract predictive structure from visual experience and organize it in natural language for explicit reasoning, we learn physical language from in-the

VideoCoCo: Code-as-CoT for Physically-Consistent Video Generation via an Agentic Dual-Engine System huggingface.co

Text-to-video models have achieved remarkable visual quality, yet they still struggle to generate physically consistent dynamics because the temporal evolution of a scene must be inferred implicitly from a highly compressed text prompt. Existing chain-of-thought approaches introduce intermediate plans or visual states, but these representations are typically non-executable or temporally sparse, limiting their ability to instantiate and control the complete spatiotemporal process. To address this

ShadowDancer: Teaching Video World Models Any Action by Learning Unified Dynamics Representations from a Video and Its Shadow huggingface.co

We present ShadowDancer, a novel approach to any-action, frame-level control of interactive video world models. The obstacle is representational: existing interfaces either encode an action loosely, leaving how it unfolds for the model to improvise, or encode it exactly through structured signals that serve one family and are hard to acquire, so precise control across diverse dynamics remains impractical. Demonstration videos are the natural remedy, specifying any dynamics frame by frame; yet a

β-OPSD: Deriving with Policy Optimization, Training with Self-Distillation huggingface.co

On-policy self-distillation (OPSD) is a promising approach to improve reasoning language models, but it remains brittle in practice: making it work reliably often requires substantial engineering effort. We identify a structural source of this difficulty: vanilla OPSD is precisely the β=1 member of a broader policy-optimization family, where β weights the KL penalty anchoring the student to a reference policy. This equivalence turns β from an implicit value fixed at one into a controllable regul

INTACT: Isomorphic Intent-to-Action Learning for Search-Free World Models huggingface.co

Forward latent world models predict how actions change a scene, but recover actions for a desired change only through expensive test-time search. We introduce INTACT (INtent-To-ACTion), an end-to-end JEPA that turns action-labeled, reward-free trajectories into a deployable intent-to-action interface. Each transition supplies physical intent z_{t+1}-z_t, while a future goal supplies deployment intent sg(z_g)-z_t. The architecture is isomorphic between the local and goal motion-intent backbone-in

Flux-OPD: On-Policy Distillation with Evolving Contexts huggingface.co

Large language model training in open-ended domains lacks verifiable rewards, making task preferences difficult to formalize as effective supervision. Contexts can convey such preferences, yet provide little additional supervision once distilled into the student, motivating contexts that evolve with student performance. However, directly using evolving contexts as in-training supervision results in an unstable distillation target and conflicting distributions, requiring mechanisms to stabilize t

ACE-Data-0: Human-Centric Ambient Capture as Embodied Data Engine huggingface.co

Embodied intelligence faces a fundamental data bottleneck. Models must capture how first-person perception, whole-body motion, dexterous manipulation, object state, sound, and touch evolve together as humans pursue goals over time. Existing datasets fragment this experience across viewpoints, modalities, or spatial scales, leaving the full perception-action loop only partially observed. We introduce the Ambient Capture Engine (ACE), a human-centric data engine that transforms real home environme

OmniScope: Modality-Decoupled Token Compression for Omnimodal Large Language Models huggingface.co

Existing token compression methods for omnimodal large language models typically rely on one modality to determine what to retain in the other. We show that this assumption often breaks down: for the same query, audio and video relevance often peaks at different moments. This cross-modal salience mismatch makes unidirectional guidance prone to discarding answer-critical cues under aggressive compression. We propose OmniScope, a training-free token compression framework that uses the query as a s

ReToken: One Token to Improve Vision-Language Models for Visual Retrieval huggingface.co

Long visual context poses a challenge for vision-language models: performance degrades as the number of distractors grows, and processing all tokens at once is computationally infeasible under GPU memory constraints. We present ReToken, a single learnable embedding trained as an explicit retrieval target that selects a sparse set of query-relevant visual tokens from a pre-filled visual KV cache. Trained on only a small image-QA dataset, ReToken yields consistent gains across image and video benc

See2Think: Do Multimodal Models Really Use Intermediate Visual States? huggingface.co

Multimodal large language models increasingly use sketches, annotations, tools, and intermediate images during reasoning, but it remains unclear whether they truly rely on these visual states. Existing benchmarks are limited both by task collections with narrow coverage or partially text-solvable samples and by evaluations that emphasize final answers without diagnosing how intermediate visual states are generated, rendered, and used. We introduce See2Think, a unified evaluation framework compri

AskChem: Claim-Centered Infrastructure for Chemistry Literature Synthesis huggingface.co

Chemistry literature synthesis often requires assembling specific findings scattered across many publications, yet existing literature-search systems primarily return ranked document lists. As a result, scientists and AI agents need to locate relevant information, verify their provenance, and assemble cross-paper answers manually. We present AskChem, a claim-centered infrastructure for cross-paper chemistry search. AskChem changes the unit of retrieval from the paper to the provenance-carrying c

MPIE-Bench: Benchmarking Anatomically Plausible Multi-Person Interaction Editing huggingface.co

Text-to-image and personalized editing models now synthesize high-fidelity single-subject images with ease. Yet placing multiple named people into shared contact actions such as embrace, carry, or grapple still exposes major failures: fused limbs, invented extremities, and interpenetrating bodies. Existing evaluations largely overlook these anatomical and geometric issues, and VLM-as-a-judge checklists often saturate on Interaction while the errors remain obvious to humans. We introduce MPIE-Ben

Can Large Language Models Execute Parent Orders? huggingface.co

Parent-order execution is a core problem in algorithmic trading, where the goal is to split a large order into smaller orders while reducing execution costs. Existing approaches either rely on pre-specified market assumptions that may not hold in practice, or require task-specific training that limits adaptability to new settings. To overcome these limitations, we present the first systematic study of large language models (LLMs) for parent-order execution. This extends the use of LLMs in financ

RefCaptioner: Multi-Reference Image-Grounded Video Captioning huggingface.co

Existing video captioning models generate natural descriptions of video content but cannot explicitly ground local visual elements to multiple reference images. We introduce multi-reference image-grounded video captioning, a new task requiring factual video descriptions with phrase-level reference grounding, and propose RefCaptioner, a two-stage post-training framework for this task. RefCaptioner combines mixed-data SFT with Hierarchical Coverage-Discounted GRPO to jointly improve reference sele

Beyond Borrowed Histories: Person-Aligned User Simulation for Interactive Role-Playing Evaluation huggingface.co

Role-playing agents (RPAs) have become one of the most important consumer applications of large language models. Users engage in multi-turn conversations with RPAs for experiences such as emotional comfort, making reliable evaluation essential for measuring capability, comparing systems, and guiding further improvement. Existing benchmarks, however, typically require an RPA to continue a fixed dialogue history and then evaluate the continuation using a fixed rubric detached from the user. We ide

AMRD: Adaptive Multi-Teacher Relational Distillation for Lightweight Speech Emotion Recognition huggingface.co

On-device speech emotion recognition (SER) is critical for real-time applications, yet large self-supervised models that excel at SER are too costly for edge devices. Multi-teacher knowledge distillation can compress them into a lightweight student, but two challenges remain: teacher reliability varies across batches, and logit-level distillation ignores inter-sample relational structure. We propose Adaptive Multi-teacher Relational Distillation (AMRD) to address both. A one-class SVM on each te

AI Tour Meeting: Group Travel Planning by LLM Agents huggingface.co

This paper proposes AI Tour Meeting, a group travel planning framework powered by multiple Large Language Model (LLM)-based agents. The agents are instantiated with distinct personas and collaboratively seek an itinerary that satisfies their constraints and preferences through natural language discussion. The framework enables easy and flexible orchestration of such discussions by providing interfaces for configuring agent personas, discussion workflows, monitoring, and LLM deployment. Its prima

Pedestrian Archetypes Extension — More Pedestrian Models for Autonomous Vehicle Safety Testing huggingface.co

In our prior work, Pedestrian Archetypes, we defined pedestrian archetypes as collections of behaviors that uniquely identify a specific type of pedestrian. The first paper proposed 12 pedestrian archetypes, including the Wanderer, Drunk, Distracted, Flash, Indecisive, Blind, Flock, Jaywalker, Elderly, Kid, Eventful, and Parked Pedestrian. These archetypes were introduced to move beyond single behavior labels and provide a more natural way to describe how dangerous pedestrians actually behave pr

References

MarkTechPost — FGN technical deep dive marktechpost.com

FGN is a ~180 million parameter Graph Transformer with 24 layers operating at a 6-hour timestep, roughly 8x faster than the diffusion-based GenCast (57M params, 12-hour steps), and outperforms GenCast on 99.9% of variables and lead times.

Physics World — extreme-weather critique physicsworld.com

AI weather models excel at average skill but frequently underperform traditional numerical models like HRES when predicting record-breaking extremes, systematically erring toward normality on out-of-distribution ‘black swan’ events.

PreventionWeb / Rice University study preventionweb.net

AI models frequently underestimate storm strength, particularly maximum wind speeds and minimum central pressures, producing overly smooth forecasts that lack the sharp detail required to represent a cyclone’s compact inner core.

Cyc-Attack (arXiv 2508.15724, Michigan State) arxiv.org

Altering less than 0.1% of global weather measurements — comparable to tampering with one out of a hundred active satellites — can effectively hide an approaching hurricane or fabricate a false landfall using gradient-based adversarial perturbations against DLWF models like GenCast and WeatherNext.

NHC 2025 Verification Report nhc.noaa.gov

GDMI [WeatherNext] was on average 140 km closer to actual cyclone location than the leading physics-based ensembles, with 72-hour Atlantic track errors dropping below ~80 miles and 5-day Melissa track error near 100 nmi — roughly 50% below the five-year average.

ArticSledge — ECMWF hybrid response articsledge.com

ECMWF operationalized its own AIFS alongside physics-based IFS and built AIFS-TC, a machine-learning system that ‘corrects’ intensity errors in standard AI outputs, achieving intensity predictions comparable to top operational guidance.

MLEvolve project page (internscience.github.io) internscience.github.io

MLEvolve secured the top spot on the full 75-task MLE-bench set, achieving a 65.3% overall medal rate within a 12-hour runtime budget… 80.3% medal rate on low-complexity tasks

Sally Liu, ‘Deep dive on OpenAI’s MLE-bench’ (Medium) sallysliu.medium.com

a full metric reading costs approximately $48,000… independent analyses suggest a persistent generalization gap of 9-13% between validation and test sets

Cloud Security Alliance, ‘AI Recursive Self-Improvement: Security Implications’ labs.cloudsecurityalliance.org

Frontis-MA1 represents a ‘security-relevant threshold’ where AI materially participates in developing successor systems under human supervision

daily.dev, ‘Kimi K3 matches GPT-5 on benchmarks, costs 4x less’ daily.dev

Kimi K3 is a 2.8-trillion-parameter Mixture-of-Experts model that activates only 104B parameters per request… its 1.56TB compressed size means running the full model at useful speeds requires a 16x GB10 cluster

NHIMG, ‘AI Coding Benchmarks and SWE-bench: Are Your Tests Predictive Enough?’ nhimg.org

contamination from training data can inflate scores by as much as 15-20 points… OpenAI recently withdrew its support for the ‘SWE-bench Verified’ subset after discovering that over 60% of the remaining tasks were effectively unsolvable or that models were simply reproducing ‘gold patches’ seen during training

FrontisAI, ‘Awesome Self-Improving Agents’ survey site frontisai.github.io

making agents smarter after deployment is essentially a ‘trace-to-capability’ problem where interaction experience must be converted into durable system updates

GitHub: onyx-dot-app/EnterpriseRAG-Bench github.com

EnterpriseRAG-Bench is a comprehensive benchmark comprising over 500,000 synthetic documents across nine source types, including Slack, Jira, GitHub, and Confluence… simulates a realistic company (‘Redwood Inference’) by introducing cross-document coherence.

r/LocalLLaMA discussion of EnterpriseRAG-Bench reddit.com

Onyx’s internal system has reportedly outperformed commercial giants like ChatGPT Enterprise and Claude Enterprise on specific workplace tasks… some suggest that high-fidelity retrieval still requires expensive multi-stage pipelines involving rerankers and hybrid search.

tianpan.co, ‘Hybrid Search in Production’ tianpan.co

Adding BM25 keyword matching to a pure vector system can jump accuracy from roughly 60% to 85%… hybrid retrieval using Reciprocal Rank Fusion (RRF) to combine BM25 and dense signals is now the recommended default for 90% of production systems.

arXiv 2508.11758 — ‘Beyond the Reranker’ critique arxiv.org

New RAG methods appear superior only because they are compared against ‘vanilla’ baselines like BM25 while omitting the industry-standard ‘Dense Retrieval + Cross-Attention’ pipeline… a reranker cannot recover a relevant document that was never surfaced in the initial retrieval step.

FalkorDB blog on GraphRAG economics falkordb.com

Early 2024 estimates for indexing a 5GB dataset reached as high as $33,000 due to exhaustive community summarization… by mid-2025, optimizations like LazyGraphRAG and smaller models reduced these costs by 99.9%.

Daily Dose of DS on file-system agents blog.dailydoseofds.com

Every tool call adds output to the context window… agents may enter a ‘control loop’ failure where they issue repetitive or cascading tool calls (e.g., recursive list_dir or repeated grep for the same symbol) without converging on an answer, exponentially increasing token costs and latency.

Sources

References

Jack Sun, writing.