Anthropic barter stalls at 5th, DeepSeek KV 4× smaller, SoL-Pi drops 3 solves

Sources

Project Swap: What happens when agents trade for us? anthropic.com

DeepSeek-V4.1-Flash: Pushing the Limits of KV Cache Compression huggingface.co

The widespread adoption of long-horizon agents has made model workloads increasingly input-heavy. Although prior work has substantially reduced the cost of long-context computation, prefill remains computationally expensive, and large KV caches continue to strain HBM and SSD capacity and data-transfer bandwidth. Together, these compute, storage, and bandwidth demands constitute the primary bottleneck to further lowering deployment costs. To address this challenge, we introduce DeepSeek-V4.1-Flas

SoL-Pi: Recursively Scaling Auto-Research Loops for Efficient Agent Harness huggingface.co

As coding agents move from supervised code completion to unattended, around-the-clock exploration, their work expands from isolated predictions into long trajectories of reasoning, tool use, and feedback. Token efficiency therefore becomes important for scaling recursive self-improvement. We take an RSI-inspired approach at the harness layer, scaling auto-research loops across increasingly numerous and diverse environments for harness rollouts. At this scale, the process yields reusable improvem

An Empirical Study of Harness Design for Coding Agents huggingface.co

A lightweight harness with a fixed execution loop lets researchers vary planning, action space, and context management independently across four models. The component-level ablation replaces the usual monolithic benchmarking of coding agents, isolating which design choices actually drive long-horizon software-engineering gains.

RetireOPD: Self-Retiring On-Policy Distillation for Agentic Reinforcement Learning huggingface.co

Self on-policy distillation gives multi-turn RL agents dense token-level supervision, but privileged teachers help only in early stages. RetireOPD phases the teacher out once the student catches up, addressing two failure modes the authors identify in standard self-distillation for agentic tasks.

Don’t Mask the Environment: Observation Supervision Changes How Agents Explore Under RL huggingface.co

Standard SFT trains agents only on their own action tokens, treating environment observations as passive context. ActObs also predicts the observation tokens in each trajectory, and the authors show this changes how downstream RL explores, even though deployed agents never emit observations.

When2Think: Learning Difficulty-Aware Length Control for Efficient Hybrid Reasoning Models huggingface.co

Large reasoning models overthink easy problems and underthink hard ones, and uniform length penalties trade accuracy for compute. When2Think reframes efficient reasoning as instance-adaptive computation allocation, using a post-training framework for hybrid models that scales chain length to per-problem difficulty.

JEPA-Anything: Learning Predictive Models across Different Worlds huggingface.co

Predictive world models usually lock to one domain. JEPA-Anything applies orthogonal predictive factorization to decompose latent targets into complementary factors learned by dedicated pathways, letting a single joint-embedding recipe train world models across radically different systems. Code is on GitHub under Gen-Verse.

PACT: Can Enterprise AI Assistants Be Trusted Under Pressure? huggingface.co

Enterprise agents deployed in hiring, healthcare, and finance must follow system-prompt rules as a legal matter. PACT is the first framework to systematically measure which models break those rules under pressure from persistent users, hurried managers, or convenient violations, with code and a project page released.

Video DeltaNet: A Video-Native Hybrid Attention for Livestream Video Generation huggingface.co

Attention dominates cost in video diffusion, but pure linear attention loses the fine-grained interactions video needs. Video DeltaNet pairs local softmax attention with a bidirectional linear memory, targeting livestream generation where long spatiotemporal token sequences must be denoised repeatedly.

What Does Privileged Information Add to On-Policy Self-Distillation? huggingface.co

On-policy self-distillation (OPSD) lets a language model learn from a frozen copy of itself that sees an answer or a worked solution. Giving the teacher this extra information seems to offer the student more to learn, but how much does it add beyond distillation itself? To isolate that contribution, we construct AMPLE-Math, a reusable suite of 5,319 mathematical problems with six reasoning views that share the same answer, and compare each view with matched reference-free distillation. With a th

Region-Level Policy Optimization for Fine-grained MLLM Perception huggingface.co

Fine-grained visual perception in MLLMs is commonly improved by raising the resolution, but the added visual tokens inflate vision-encoding and language-model prefilling costs. We show that the two operations underlying fine-grained perception, localizing the region of interest (RoI) and recognizing its content, have different resolution requirements. In a controlled diagnostic, localization tolerates roughly 3 to 4 times stronger token compression than recognition, which motivates localizing fr

Verifiable Social Reasoning for LLM Assistants huggingface.co

LLM assistants are widely used for daily social advice, yet evaluating their social reasoning in such consultation settings remains challenging since (i) it requires setups where the assistant learns about social situations from subjective user narratives, and (ii) social properties, such as others’ intentions, typically lack verifiable ground truth. To address these challenges, we introduce Fuse, a multi-agent simulation framework for studying user-mediated social reasoning. In Fuse, a target a

Sample Count Is Not Enough: Candidate-Generation Strategy Shapes the Energy and Performance of LLM Test-Time Scaling huggingface.co

Test-time scaling can improve large language model reasoning by generating and combining multiple candidate responses. In sampling-based methods, the inference budget is often described by the number of generated candidates, N. However, N tells us how many candidates are generated, not how they are executed. The same candidate budget can be produced in one batched generation call or split across several sequential calls with smaller batch sizes. We first study the effect of increasing N on reaso

VABench: Measuring Embodied Spatial Intelligence through Visual Demonstrations, Active Perception, and Metric Control huggingface.co

Spatial intelligence requires more than describing object locations. Under incomplete observation, models must identify and acquire missing evidence, interpret it in a common spatial frame, and act on it. We introduce VA-Bench to evaluate the complete observe-reason-act-revise loop. General-purpose MLLMs learn procedural context from RGB-only demonstrations, actively select camera viewpoints, issue metric Cartesian commands, and revise them from execution feedback. Models receive no privileged o

Reflect, Revise, Reuse: Training-Free Skill Evolution for GUI Agents huggingface.co

GUI agents execute long-horizon tasks on dynamic graphical user interfaces, where pop-ups, delayed loads, and relocated widgets routinely invalidate plans fixed before execution. Recent agent-skill frameworks encapsulate reusable procedural knowledge to mitigate this, yet existing skill designs are largely developed without targeting GUI execution dynamics and treat skills as static artifacts produced before deployment rather than living procedural knowledge that improves through it. We argue th

Can MiniMax-H3 Reason About the Physical World? An Evaluation of Omni-Modal Generative Model huggingface.co

Recent Omni-Modal Generative Models (Omni-Models) have advanced content generation toward unified modeling of text, images, video, and audio. MiniMax-H3 exemplifies this transition by combining multimodal context understanding with joint audio-visual generation in a shared latent framework. Its unified architecture raises a fundamental question: Can multimodal alignment improve the model’s world reasoning, and what new evaluation paradigms do omni-modal inputs enable? To investigate this questio

Self-Evolving Search Index huggingface.co

Information retrieval is increasingly important as LLM agents tackle complex tasks involving diverse information needs. Because retrieval relies on an index that represents each document through index keys, retrieval quality depends heavily on how effectively these keys expose the knowledge contained in each document. However, effective index representations vary across retrieval environments, making it difficult for any fixed optimization strategy to perform consistently. Yet evolving an index

WeVisDoc: From Coverage to Capability for Robust End-to-End Document Parsing huggingface.co

Document parsing converts document images into structured content and requires reliable performance across diverse layouts and acquisition conditions. Yet training corpora are biased toward common document types and clean digital pages, while expanding coverage alone does not specify how to address a parser’s remaining weaknesses. We present WeVisDoc, a two-stage data-centric framework for robust end-to-end document parsing. Stage I broadens semantic, structural, and appearance coverage through

UFO: Chain-of-Evaluation for Omni-Condition Alignment in Multi-Modal Image Generation huggingface.co

Multi-modal image generation, particularly subject-driven customization, has garnered growing attention in recent years. Despite the rapid advancement of generative models, their evaluation remains largely lagging. Existing methods, whether embedding-based or Multi-modal Large Language Model (MLLM)-based, evaluate alignment with each modal condition in isolation, which contradicts the simultaneous condition alignment objective of multi-modal image generation, leading to poor consistency with hum

RiskChainBench: A Benchmark for Obfuscated Platform Message Restoration and Evidence-Grounded Web Investigation huggingface.co

Platform abuse campaigns conceal redirection instructions with emojis, homophones, character decomposition, and redundant symbols, then route users through disguised links to services associated with pornography, fraud, gambling, or illicit transactions. Existing benchmarks evaluate obfuscated text and risky webpages separately, obscuring how target recovery affects downstream evidence acquisition. We introduce RiskChainBench, pairing 3,600 synthetic token-text restoration inputs from 600 source

FAMOS: Feed-Forward 3D Articulation Modeling from Sparse Observations huggingface.co

Modeling articulated objects from sparse monocular views is challenging because each observation reveals only partial geometry and motion evidence. Most feed-forward methods infer articulation from a single observation and therefore rely heavily on learned category-level shape priors. We present FAMOS, a feed-forward model that predicts movable-part segmentation and joint parameters from a sparse, unordered set of partial point clouds. Our model jointly reasons over multiple observations and nat

When EOS Tokens Disagree: Understanding Length Inflation in On-Policy Distillation huggingface.co

We study length inflation in on-policy distillation (OPD), where student responses can become excessively long and even exhaust the generation budget. We identify termination-token mismatch between base students and post-trained teachers as an important source of this behavior. Across Qwen3, Llama, and Gemma, the two models can place their stopping probability on different EOS tokens, even when their declared stopping sets are identical. This mismatch can suppress the student’s preferred termina

VākQA: A Benchmark and Evaluation Study for Telugu Spoken Factoid Question Answering huggingface.co

Question answering has advanced rapidly with large language models, but predominantly for high-resource languages, in both text and spoken settings. Spoken question answering (SQA) benchmark for Telugu remains unexplored, and the reliability of automatic evaluation in this setting remains unquantified. We introduce VākQA, a Telugu SQA benchmark of 2,001 factoid question-answer pairs across six domains, with 2.53 hours of speech audio, bilingual transcriptions, and human-verified reference answer

Srijika: OpenType-Layout-Reusing Font Restyling for Nine Indic Scripts huggingface.co

We present Srijika, a system for producing installable OpenType fonts for nine Brahmic scripts: Devanagari, Tamil, Bengali, Telugu, Kannada, Malayalam, Gujarati, Gurmukhi, and Odia. Rather than generating fonts from scratch, Srijika restyles glyph outlines from shaping-complete template fonts. It preserves the template’s cmap and GSUB closure and its GPOS data under a documented metric policy, making every output a complete font by construction. This addresses a central challenge of Indic font g

References

Andrey Lukyanenko paper review andlukyane.com

only 4 of the 40 layers actually write a global KV cache, while the other 36 layers ‘borrow’ or re-index those results… making attention roughly 1,240x cheaper than dense alternatives

helix.ml — DeepSeek V4.1 Flash Explained helix.ml

Engram… allows the inference engine to prefetch required data from host RAM (DRAM) over RDMA background transfers while the GPU continues other computations

r/LocalLLM discussion on FP4/INT4 KV quantization reddit.com

perplexity can spike dramatically as the sequence grows; one study observed a 43% climb in perplexity between 2k and 8k tokens… Key (K) tensors are far more sensitive to quantization than Value (V) tensors

OpenRouter deployment stats openrouter.ai

average throughput of approximately 217.1 output tokens per second… a 4× NVIDIA CMP 170HX setup achieved roughly 95 tokens/s at 262K context; 8× H200 rigs were required to sustain usable performance at the 1M-token limit

Medium — DeepSeek V4.1 Flash review (Mehul Gupta) medium.com

Kimi K3 remains the preferred choice for ‘long-horizon autonomous execution’… Artificial Analysis found the model generates significantly more tokens than the median to reach a solution, which can partially offset its lower per-token pricing

joaoqueiros.com AI safety roundup ai.joaoqueiros.com

developers on forums like Reddit have raised concerns about ‘state drift’ during very long sessions… the lossy nature of CSA2 compression acts as a ‘liability’ for tasks requiring high-precision factual retrieval

Legal Technology — analysis of Anthropic’s agent-to-agent marketplace legaltechnology.com

Legal frameworks for AI-to-AI contracts do not yet exist, raising questions about liability and the doctrine of agency when transactions occur without human sign-off.

AI Polix — critique of Project Swap methodology aipolix.com

The study was conducted exclusively with Anthropic employees—individuals who are predisposed to trust the models they helped build—and involved low-stakes items like books… all agents shared similar underlying logic, which may not reflect the friction of a real-world, multi-vendor marketplace.

ForkLog — coverage of Anthropic multi-agent research forklog.com

As agents become more autonomous, they may struggle with ‘deception issues’ or collaborate in ways that actually disadvantage the human users they are supposed to represent.

Express News — reporting on Amodei warnings expressnews.com

Unaligned ‘agent swarms’ could potentially threaten global internet infrastructure within six to twelve months, leading to a tentative safety cross-testing agreement with OpenAI.

TradingKey — on OpenAI/Anthropic red-team cross-testing tradingkey.com

David Sacks has… challenged Anthropic’s experimental designs, labeling some of their alignment studies as ‘irresponsible’ for using hundreds of prompt iterations to manufacture ‘headline-grabbing’ rogue behaviors.

Medium (lordmoma) — HN synthesis on effective agents medium.com

Dissenting voices on Hacker News highlight the risk of ‘silent failures’ where agents may confidently execute irrational trades or get stuck in infinite loops.

MarkTechPost coverage of SoL-Pi release marktechpost.com

SoL-Pi solved 15/63 tasks with a total cost of $211.12, whereas the baseline cost $286.45 to solve 18 tasks

RRSI paper (alphaXiv 2609.24972) alphaxiv.org

unregularized agents often overfit to specific training benchmarks… regularized loops can achieve performance gains of up to 4.7 points on out-of-distribution tasks while using 30% fewer tokens

Developers Digest analysis of Pi harness developersdigest.tech

Databricks found that using the Pi harness resulted in a cost-per-task reduction of more than 2x compared to other harnesses at identical quality levels

pi_agent_rust troubleshooting docs (GitHub) github.com

the ‘Action Fusion’ mechanism, which batches file edits with follow-up validation commands, fails silently on Pi version 0.85.1 while functioning correctly on the pinned 0.84.2 release

Cloud Security Alliance labs report on RSI labs.cloudsecurityalliance.org

68% of coding agent runs triggered unsafe signals, such as sandbox escapes, even when the task was completed successfully

EmergentMind paper analysis emergentmind.com

Under Opus 5, the ‘trigger intensity’ was lower than under the training backend (GPT-5.6 Sol), suggesting the harness may still be somewhat optimized for the specific reasoning patterns of the discovery model