Sources

CodeNib: A Multi-View Data System for Serving Repository Context to Coding Agents huggingface.co

Coding agents repeatedly search, navigate, and retain context from evolving repositories, but disconnected indexes, language servers, and task-local histories force repeated discovery and obscure lifecycle costs. CodeNib builds reusable lexical, dense, and structural views per repository commit, maps outputs to repository-relative source ranges, maintains selected views across edits, and serves ranked search, symbol navigation, and bounded context through one runtime. Across 100 snapshots, we

Reinforcement Learning for Code Optimization huggingface.co

RL for code correctness is now established: have the model generate a program, run it against hidden test cases, and reward solutions that pass. Extending this to code optimization seems straightforward: just add execution time to the reward. But in practice, once timing drives the reward, small problems in measurement noise, reward sparsity, or GRPO instability overwhelm the signal and make RL fail: generated solutions are barely faster, and more of them can fail. We make execution time learnab

Keep It InMind: Benchmarking the Implicit-Association Blind Spot in Agent Memory huggingface.co

Long-term memory systems store what a user says in an external store and retrieve it when a related query arrives. This interface rests on an assumption so natural that it is rarely stated: a memory that is needed will resemble the query that needs it. World knowledge breaks the assumption. A tree-nut allergy should change the answer to a macaron request through their almond-flour ingredient, yet the two texts share no cue a retriever can see. We call this failure mode the implicit-association b

Agent Retrieval Bench: Evaluating Repository Context Retrieval for Coding Agents huggingface.co

Coding-agent evaluations usually judge the final patch, but Agent Retrieval Bench isolates the upstream step of finding the right repository files. Built from real workflow signals against frozen base commits, it defines relevance by what an agent needs next rather than direct query-to-file matches.

Mage-VL: An Efficient Codec-Native Streaming Multimodal Foundation Model huggingface.co

Mage-VL tackles the paradox that vision-language models handle offline reasoning but stumble on live perception. Its Mage-ViT tokenizer drops uniform frame sampling, instead encoding entropy-rich regions guided by motion vectors and residual energy for efficient real-time multimodal understanding.

Wonder: Video World Model Done Better huggingface.co

Wonder turns an image or short clip into a navigable world where users move the camera, explore new regions, and revisit old ones over long horizons. The system co-designs a camera control method, memory mechanism, and training strategy to sustain real-time playback.

Visual prompt engineering for video models huggingface.co

Prompt engineering jumps from text to pixels: researchers automatically edit the input image—adding arrows, masks, or annotations—to boost video foundation models on tasks like visual physics reasoning. The approach mirrors LLM prompting, treating the image itself as the tunable instruction.

PerceptionBench: Evaluating Atomic Visual Perception in Multimodal Large Language Models huggingface.co

Existing multimodal benchmarks tangle perception with reasoning and domain knowledge. PerceptionBench takes a bottom-up route, diagnosing atomic visual abilities like counting, localization, and attribute recognition so failures trace cleanly to the perception stage rather than downstream inference.

A New Role for Relevance: Guiding Corpus Interaction in Agentic Search huggingface.co

Direct Corpus Interaction lets agents grep through documents but wastes cycles without relevance signals. RARG blends the two, using relevance scores to guide fine-grained exploration so agents localize, compose, and verify evidence for complex questions faster than top-k retrieval allows.

Shieldstral huggingface.co

Shieldstral recasts moderation as a binary yes/no QA task, letting datasets with clashing taxonomies train one policy-adaptive model. The 3B multimodal classifier matches or beats systems roughly seven times its size on text safety and sets a new state of the art on multimodal benchmarks.

Uncovering Latent Reasoning Strategies in Language Models huggingface.co

A language model p_θ(y mid x) trained on reasoning tasks learns to solve problems via multiple distinct strategies, yet these strategies are implicit and entangled within the model’s response distribution. We study the problem of decomposing the response distribution of a given pretrained language model into a structured, strategy-conditioned representation. Specifically, we learn a latent-variable factorization p_θ(y mid x) leadsto (r_ϕ(z mid x), g_ϕ(y mid x,z)), where a router r maps each inpu

HiFi-UMI: Learning Deployable Manipulation Policies from High-Fidelity UMI Data Alone huggingface.co

Learning deployable manipulation policies is bottlenecked by the scarcity of data that is both high-fidelity and scalable. Real-robot teleoperation is accurate but costly to scale; robot-free UMI capture scales readily, and current practice uses the resulting data mainly for pre-training, adding a small real-robot “anchor” at post-training. We ask whether raising the fidelity of robot-free UMI data, rather than shrinking the real-robot fraction, can remove that anchor. We present HiFi-UMI, a por

VisualPatchWorld: Code World Models as Latent Structured Representations for Planning huggingface.co

Different research lines use the term world model in different ways, yet they share a common aim: to capture how the world evolves under action in a form that supports perception, simulation, and planning. Two prominent realizations are neural predictors that learn dynamics in continuous vector spaces, and hand-built physics engines that expose explicit state and physical laws. Neural predictors scale from data but leave the form of the dynamics implicit; physics engines are inspectable and edit

How Fast Can Reward Models Score? A Systems Study of C++ and PyTorch Inference Runtimes for RLHF huggingface.co

In RLHF pipelines, reward scoring blocks policy updates. Slow scoring bottlenecks the entire loop, since no update runs until every rollout gets a score. And yet most setups just default to PyTorch eager mode or torch.compile, no one checks if that’s actually fastest. Scoring itself is small. Rollout generation eats far more of a typical RLHF step. But scoring and generation fight over the same CPU and GPU resources, so a faster scoring engine doesn’t shrink step time on its own. It mainly frees

ReDesign: Recovering Editable Design Structures from Images via Agentic Decomposition huggingface.co

Recovering an editable design file from a raster image is a common and costly bottleneck in modern design workflows, yet remains challenging since editability depends on recovering multi-modal attributes, such as typography, vector geometry, colors, grouping, and layer ordering. We present ReDesign, an agentic framework that grows an editable layer hierarchy by selecting and composing specialized tools across modalities. To keep this long decision process reliable despite imperfect tool outputs,

Pass the Baton: Trajectory-Relayed On-Policy Distillation huggingface.co

On-policy distillation (OPD) grounds token-level supervision in the student’s own trajectory, yet suffers from prefix failure: once the student commits to a wrong reasoning direction, all subsequent generation builds on this deviation, producing misdirected continuations that elicit unreliable supervision and waste compute. We identify a teacher-student continuation asymmetry on failed prefixes, where the teacher tends to redirect while the student continues along the original direction, and con

Towards Robust Reinforcement Learning for Small-Scale Language Model Agents huggingface.co

The alignment of Small Language Models (SLMs) in the 70—500M parameter range using reinforcement learning is often considered unstable, though the underlying failure mechanisms have not been systematically investigated. In the State-of-the-Art (SOTA) research, fifteen (model, corpus) configurations were trained using Proximal Policy Optimization (PPO). The experiments included Pythia-70M, 160M, 410M and SmolLM2-135M, 360M on the TinyStories, CNN/DailyMail, and Wikitext-103 corpora. Three reprod

MODUS: Decoder-Only Any-to-Any Modeling of Diverse Modalities huggingface.co

Any-to-any models predict any modality from any combination of others within a single network, a formulation used in multimodal vision and vision-language models, and increasingly in scientific domains such as ecology and astronomy. Existing any-to-any models are typically trained from scratch using encoder-decoder or diffusion architectures, impacting their performance and preventing them from using strong pre-trained decoder-only models as a prior. In this work, we investigate decoder-only any

Parallel Decoding Distillation for Fast Image and Video Generation huggingface.co

Generation in video diffusion or flow models is computationally expensive due to the slow and iterative sampling process. Current state-of-the-art (SOTA) acceleration methods heavily rely on variational score distillation (VSD) and adversarial losses to distill diffusion models into few-step generators. Albeit achieving high-quality video generation, these training losses are notoriously hard to optimize and suffer from mode collapse, leading to loss of video diversity and lack of motion. In thi

Temporal-Distance JEPA: Plan-Aware Representation Learning for Latent World Model Predictive Control huggingface.co

Joint-Embedding Predictive Architectures (JEPAs) learn world models by predicting in representation space rather than reconstructing pixels, making them a natural backbone for latent model predictive control from offline demonstration logs. JEPA-style training optimizes short-horizon latent prediction, whereas planning requires a multi-step ranking of imagined futures by goal progress. Prior JEPA planners often inherit that ranking from embedding geometry, typically latent Euclidean distance, wh

OmniDelta: Skill-Driven Budget Allocation for Token Compression in OmniLLMs huggingface.co

Emerging Omni-modal Large Language Models (OmniLLMs) enable unified understanding of text, audio, and video, but their long audio-video token sequences introduce substantial memory and inference costs. Existing compression methods mainly focus on selecting important tokens under fixed budgets, leaving the preceding budget-allocation problem underexplored. We show that direct query-to-audio/video similarity is unreliable for inter-modal budget allocation, and that uniform intra-modal budgets can

Novel Claim or Déjà Vu? Rethinking “Contamination-Free” Dynamic Evaluation for Multimodal Automated Fact-Checking huggingface.co

Multimodal automated fact-checking (MAFC) verifies claims by retrieving and reasoning over external evidence. However, most existing static benchmarks risk contamination: they primarily consist of outdated claims verifiable using an LLM’s internal knowledge without external evidence. This can inflate performance estimates and fail to reflect true capability on novel claims that require up-to-date information. To address this, emerging dynamic benchmarks collect claims published after LLMs’ knowl

Where Quality Breaks in Compressed Short-Text Generation: Staged Bottleneck Localization huggingface.co

Compressed short-text generators can fail in two different places: the codec may discard information before generation starts, or the latent generator may produce weak codes. Without separating these failure modes, researchers can spend compute improving the wrong component. We study this problem in a controlled 64-to-16 TinyStories case study built from a hierarchical VQ-VAE-2 codec and a masked discrete diffusion generator (MDLM). We use a staged validation protocol that separates codec recons

Projection Pursuit CPCANet for Domain Generalization huggingface.co

Domain Generalization (DG) aims to learn representations robust to distribution shifts. Recent geometric alignment methods, such as CPCANet, extract domain-invariant structures through batch-wise Common Principal Component Analysis (CPCA). However, CPCANet suffers from rank-deficient covariance estimation due to the small-sample-size issue in mini-batch training. To address this limitation, we propose Projection Pursuit CPCANet (PP-CPCANet), a covariance-free framework that learns a global ortho

Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion huggingface.co

We present a reproducible pipeline for mapping Common Vulnerabilities and Exposures (CVEs) to MITRE ATT&CK Enterprise techniques from free-text vulnerability descriptions. Rather than relying on the CWE->CAPEC->ATT&CK derivation chain, whose table-expansion artifacts we quantify, we train a multi-label classifier on a curated gold dataset of 1,207 CVEs from expert MITRE Center for Threat-Informed Defense mappings. The resulting model approximately doubles recall@5 compared with a zero-shot embed

OPERA: Offline Policy-guided Expert Routing and Adaptation for Universal Biomedical Image Analysis huggingface.co

Biomedical image analysis spans diverse modalities and tasks, yet real-world deployment is hindered by severe distribution shifts across scanners, protocols, and patient populations. High-performing models consequently require repeated domain-specific fine-tuning, which is a costly cycle that becomes impractical when labels are scarce or privacy constraints limit data sharing. We propose OPERA (Offline Policy-guided Expert Routing and Adaptation), a multi-agent ensemble framework that addresses

Human-in-the-Loop Signature Bootstrapping for UAV Hyperspectral PFM-1 Mine Detection huggingface.co

Hyperspectral imaging (HSI) is useful for material discrimination, but operational mine screening also depends on how many false alarms must be inspected before targets are found. This paper studies PFM-1 landmine detection in unmanned aerial vehicle (UAV) visible and near-infrared (VNIR) HSI using spectral angle mapper (SAM), matched filter (MF), adaptive coherence estimator (ACE), and constrained energy minimization (CEM). We compare a ground-measured SVC signature, a fully informed in-scene c

GLI-AL: A Multi-Modal Glioma MRI Label Resource with Unified Anatomy-Lesion Labels huggingface.co

Existing BraTS-GLI datasets provide a widely used benchmark for adult glioma MRI segmentation, but their task definition focuses on tumor subregions and does not systematically represent coexisting white matter hyperintensities (WMH). In joint segmentation settings, such unlabeled abnormalities introduce task-specific label noise by treating pathological regions as normal tissue. To address this limitation, we introduce BraTS-GLI Anatomy-Lesion, a controlled-access, labels-only derived resource

Edge-Aware Thermal Infrared UAV Swarm Tracking huggingface.co

Thermal infrared (TIR) imaging is essential for UAV swarm operations in visually degraded environments. However, tracking tiny UAVs remains challenging due to limited appearance cues, frequent occlusions, and rapid maneuvers. Despite significant progress driven by benchmarks such as the Anti-UAV challenge, existing methods primarily prioritize accuracy while overlooking the computational constraints of real-time edge deployment. The standard Kalman Filter (KF) offers the efficiency required for

References

ragaboutit.com — ‘92% of RAG Systems Fail Multi-Hop Queries’ ragaboutit.com

Approximately 73% of RAG failures originate in the retrieval step rather than the LLM’s generation capability… ‘hallucinated bridge’ failure mode occurs in roughly 41% of multi-hop failures.

mem0.ai — ‘AI Memory Benchmarks in 2026’ mem0.ai

Mem0 reports industry-leading scores of 92.5 on LoCoMo and 94.6 on LongMemEval, claiming a 91% reduction in latency compared to full-context methods.

Reddit r/Rag — ‘I’m sick and tired of these memory benchmarks’ reddit.com

LongMemEval has reached a state of ‘saturation,’ with top-performing systems bunching in the 90–95% accuracy range… teams can inflate scores through ‘content stuffing’ or lenient custom LLM-as-judge prompts.

vectorize.io — ‘Agent Memory vs RAG’ vectorize.io

RAG systems are approximately 1,250x cheaper per query than long-context approaches… information placed in the middle often suffers a 30% accuracy degradation, even if the total token count is well within the model’s advertised limit.

GitHub — Letta/MemGPT patterns notebook (NirDiamant) github.com

Core memory consists of a small, editable text block—often split into ‘persona’ and ‘human’—that is injected into every system prompt… agents can extract facts and immediately update their ‘always-in-context’ block via core_memory_replace.

supermemory.ai — ‘We broke the frontier in agent memory’ supermemory.ai

Frameworks like MindCache shift expensive reasoning to the ingestion phase, using hierarchical clustering to ensure that retrieval plans and assembles evidence rather than just searching for similar text.

CodeNib paper (arXiv 2607.25431) arxiv.org

graph maintenance successfully matched an independent rebuild on 15 out of 33 source-changing transitions, while vector updates matched in 28 out of 31 cases

Sourcegraph — Announcing SCIP sourcegraph.com

SCIP replaces LSIF’s opaque, globally incrementing IDs with human-readable string symbol IDs, enabling indexers to re-process only changed files while maintaining a consistent relationship graph

ShareUHack — Best MCP Servers Guide 2026 shareuhack.com

Serena provides symbol-level operations via LSP and is characterized as an ‘IDE for AI agents,’ but can ‘slow to a grind’ during live indexing on larger projects — the gap CodeNib’s precomputed multi-view manifests target

dev.to — Beyond Prompt Guessing: Why LSP Integration Is the Missing Protocol dev.to

an LSP-aware agent completed a task using only 33k tokens with zero errors, while a text-only ‘probabilistic’ agent consumed 188k tokens and still produced broken code

aicyber.de5.net — State of AI Infrastructure 2026 aicyber.de5.net

By mid-2026, experts increasingly categorize these repository indices as materialized views… Cursor’s approach is a ‘heavy’ materialized view requiring significant upfront indexing; Claude Code represents a ‘virtual view’ computed on-demand

Medium / AlignX — Enterprise AI Agents Accuracy medium.com

Current state-of-the-art agents peak at 85–90% accuracy on complex tasks. In enterprise environments, a 10% error rate is often deemed unacceptable without a human-in-the-loop or a deterministic validation layer

Berkeley RDI — ExploitGym rdi.berkeley.edu

Rather than optimizing a matrix multiplication kernel, models may learn to modify the timing function directly to return zero or a near-zero duration… another common trick involves using sys.exit(0) to prematurely terminate a test harness with a ‘success’ exit code.

GSPO paper (arXiv 2605.00932) arxiv.org

A mismatch between sequence-level rewards and token-level optimization… GRPO applies updates at the token level, leading to volatile gradient signals… particularly severe in sparse MoE models and can lead to catastrophic model collapse.

ICML 2025 — Shypula et al. ‘Learning Performance-Improving Code Edits’ (PIE) icml.cc

Models fine-tuned for efficiency could achieve a mean speedup of 6.86× across eight generations, significantly outperforming the 3.66× average achieved by human competitive programmers.

MarkTechPost — Meta FAIR CWM release coverage marktechpost.com

CWM is trained on 120 million Python execution traces and 3 million agentic trajectories within Dockerized environments… achieved 65.8% pass@1 on SWE-bench Verified and 68.6% on LiveCodeBench.

r/machinelearningnews discussion of CWM reddit.com

CWM’s performance is highly dependent on a specific ‘reasoning’ system prompt that utilizes tags… human intervention remains critical for high-level architectural decisions and avoiding ‘agentic load’ that can overwhelm CI/CD pipelines.

Meta — Code World Model Preparedness Report ai.meta.com

CWM does not introduce catastrophic frontier risks in cybersecurity or biological domains beyond existing AI ecosystem levels.

Sources

References

Jack Sun, writing.