Sources

Learning to Discover Interesting Mathematics huggingface.co

Recently, Large Language Models (LLMs) have been increasingly able to solve advanced mathematical problems, including many that have been open for decades. This opens the door to expansion of mathematical knowledge at unprecedented scale. Yet, while LLMs may be able to conjecture and prove more and more theorems, it remains open whether this new mathematical knowledge is interesting or useful. We define intrinsic interestingness of a theorem as the ratio between the length of its proof and the l

Your Transformer Can Hold Two Thoughts at Once: Evidence of Linear Superposition in LLMs huggingface.co

While Large Language Models (LLMs) rely on highly non-linear components, in this work we demonstrate that they exhibit fundamental linearity: when inputs from distinct text streams are linearly combined, the model outputs a superposition of the individual next-token distributions. We term this the Superposition Linearity Hypothesis. We provide evidence that superposition is an intrinsic property of the Transformer architecture rather than an emergent consequence of training; in fact, we observe

ExplorationBench: Measuring AI Systems’ Exploration in Verifiable Alien Worlds huggingface.co

ExplorationBench evaluates whether AI systems can form hypotheses, design experiments, and iterate on results in synthetic worlds whose rules aren’t in pretraining data. The benchmark separates genuine exploration from memorized recall by verifying new hypotheses against ground-truth world dynamics the model has never seen.

Rufus-Air: An Open LLM Post-Training Recipe huggingface.co

Built on GLM-4.5-Air-Base (106B-A12B), the pipeline runs SFT, then reasoning, coding, and instruction-following RL, three agent stages, and RLHF. The release documents data, reward design, infrastructure, and stagewise results, moving from verifiable rewards to judge-based signals as capabilities advance.

Coding Agents for Generalized Task and Motion Planning Problems huggingface.co

Generalized task and motion planning usually demands heavy TAMP-specific engineering to reuse structure across problem instances. The RoboCode project shows coding agents can automate that step, synthesizing planners that handle discrete decisions coupled to geometric, kinematic, and dynamic constraints across new instances.

Training Object Permanence in World Models huggingface.co

Video generation models show emergent reasoning but fail basic object permanence, the cognitive prior that objects persist when occluded. WROP supplies a core-cognition-inspired training corpus that instills permanence and solidity in world models, a step toward human-like physical intelligence. The paper drew 237 upvotes.

AgentKernel: The Trust-Native Agentic Operating System huggingface.co

Current agent governance runs as application middleware, sharing a process boundary with the agents it monitors — a weak defense when untrusted inputs reach privileged tools. AgentKernel proposes an OS-style trust boundary that mediates memory, inputs, and tool calls to contain prompt-injection attacks.

Parts-of-Speech as Emergent Categories in SAE Latent Space huggingface.co

Using part-of-speech tags as a controlled probe, researchers find morpho-syntactic categories are highly recoverable from sparse autoencoder activations but never map one-to-one onto individual latents. PoS distinctions instead emerge from structured groups of features, complicating the clean interpretability story SAEs promise.

PUBG Ally: A Conversational Embodied Agent as an AI Teammate huggingface.co

The embodied agent plays alongside humans in PUBG: BATTLEGROUNDS, pairing agentic tool use with real-time game control under strict latency limits. A language model drives perception, speech, and action in sync, so chatter stays aligned with what Ally is actually doing on the map.

Agent-Editing World Model: Rethinking World Modeling for LLM Agents huggingface.co

Recent advances in large language models (LLMs) have enabled agents to tackle long-horizon tasks across diverse environments. To further improve agent performance, existing language world models typically predict environment observations, yet reconstructing high-entropy, execution-dependent tool responses offers limited value when real feedback is available. Meanwhile, agents suffer from task-state contamination, where unsupported assumptions and outdated plans persist in history and distort sub

Qwen-Planner-Agent: A Closed-Loop AI-for-AI Framework for Real-World Mobile Planner Agents huggingface.co

The rapid progression of large language models is extending AI from passive content generation into the active workflows of engineering and scientific discovery. This shift raises a compelling question: can AI be both the object of development and an active participant in building next-generation AI systems? We explore this question by building Qwen-Planner-Agent within a closed-loop AI-for-AI framework for scalable development and iterative improvement. Mobile planning offers a demanding test o

Neural Spectral Capacity: Measuring and Designing Architectures from Network Specification Alone huggingface.co

Modern Transformer design and compression both reduce to allocating capacity under a budget. The standard scalars for these decisions, #Params and #FLOPs, capture size and compute but not architectural structure: two architectures with identical parameter budgets but different depth-width, head, or FFN allocations receive identical scores yet behave differently. We propose Neural Spectral Capacity (NSC), a closed-form scalar grounded in the singular-value spectrum of each weight matrix. Under st

IterSynth: Rethinking Deep Search Agents via Role-Decoupled Iterative Synthesis huggingface.co

Deep search requires LLM agents to decompose complex queries, search for evidence, and synthesize grounded answers, yet existing ReAct-style agents suffer from two limitations: role coupling, where one policy must handle planning, evidence use, and synthesis; and context accumulation, where growing search histories introduce noise and obscure useful information. To address these issues, we propose IterSynth, a role-decoupled and summary-based paradigm that alternates between a Planner for identi

DeltaWAM: Delta World Action Models for Bimanual Manipulation huggingface.co

World-action models (WAMs) transfer visual and motion priors from pretrained video generators to robot control by jointly modeling visual dynamics and actions. Existing WAMs, however, predict dense future frames during training, repeatedly modeling largely unchanged content and coupling action-conditioned dynamics to nuisance appearance variations. At inference, processing each complete observation with the heavy video expert bottlenecks few-step action generation. Accordingly, we propose DeltaW

World Action Agent: Harnessing VLMs for Robot Manipulation via World Action Rehearsal huggingface.co

General-purpose vision-language models (VLMs) bring broad knowledge and spatial reasoning to robot manipulation, yet existing systems either use them indirectly, to predict constraints or write programs, or give them a view of the scene rather than a world in which to act. We present World Action Agent (WAA), a multi-agent harness through which VLMs pilot robots with basic tools, making every decision within a visual action workspace. The workspace has three properties. Contact views, selected a

OmniEcho: Spatial Audio Understanding for Embodied Agents huggingface.co

Humans can effortlessly localize the direction of a sound source and integrate it with visual cues for reasoning, yet this remains challenging for embodied agents. In particular, it is still unclear how to effectively evaluate and model spatial audio understanding in embodied settings. To address this gap, we introduce OmniEchoBench, a unified benchmark for spatial audio-visual perception and audio-vision-language navigation. OmniEchoBench comprises six tasks over 197 real-world spatial audio-vi

WanPE: Towards Cinematic Prompt Enhancement for Modern Text-to-Video Generation huggingface.co

Video generation begins in text space by authoring a cinematic screenplay, then materializes into pixels. As contemporary video generators scale to 30 seconds and faithfully follow complex conditions, the textual prompt largely directs the production, planning how actions, camera trajectories, lighting, and sound unfold across multi-shot sequences. In this paper, we present WanPE, a 397B-parameter prompt enhancement model trained on 1.05M real-world videos to master director-level cinematic plan

ViRDM: Taming Representation Distribution Matching for Few-Step Causal Video Generation huggingface.co

Few-step autoregressive (AR) video diffusion enables low-latency streaming generation, but existing post-training methods predominantly rely on Distribution Matching Distillation (DMD), requiring both a large pretrained teacher and an online critic to estimate distributional discrepancies through diffusion scores. In this work, we ask whether this resource-intensive teacher—critic stack can be eliminated by post-training only the generator against a precomputed target distribution. Drawing insp

AV-GRPO: Modality-Anchored Decoupling Diffusion Reinforcement Learning for Joint Audio-Video Generation huggingface.co

Recent years have witnessed major progress in joint audio-video generation. Existing models still suffer from limited per-modality fidelity, insufficient text-modality alignment and weak cross-modal synchronization. While reinforcement-learning post-training offers a promising remedy, directly adapting it to joint audio-video generation is challenging. Heterogeneous multimodal rewards entangle learning signals and complicate credit assignment. Joint optimization of two modality towers is computa

Just Ask Jev: Reinforcement Learning for Calibrated Decisions as a Zero-Shot Detector of AI Alignment Failures huggingface.co

Detectors of alignment failures screen deployed language models and score alignment benchmarks. Most are generative judges that spend a decoding pass on every criterion, and classifiers that read token probabilities, such as Llama Guard, still score one fixed label per call. Jev, a model trained with reinforcement learning for calibrated decisions (RLCD), answers many typed questions about one input with calibrated probabilities in a single call. Whether it detects alignment failures has not bee

RGBD20K: A Large-Scale Benchmark for RGB-D Semantic Segmentation huggingface.co

In this paper, we propose RGBD20K, a novel dataset for facilitating the development of more robust and general RGB-D semantic segmentation by encompassing abundant categories and high-quality annotations. RGBD20K possesses several attractive properties: (1) Expanded Semantic Space. In particular, it covers 160 fine-grained categories, largely surpassing the category diversity of existing popular RGB-D benchmarks (e.g., NYUv2 with 40 classes and SUN RGB-D with 37 classes). With such enriched sema

Rate-distortion optimization for full-reference image quality metrics via stochastic Hessian estimates huggingface.co

Block-based video codecs select coding parameters based on the input by optimizing a rate-distortion trade-off. The conventional distortion choice, the sum of squared errors (SSE), simplifies parameter selection: the SSE is the sum of block-wise SSEs, so rate-distortion optimization (RDO) can treat blocks independently. Alternatively, full-reference image quality assessment (FR-IQA) metrics such as MS-SSIM or LPIPS often align better with the human visual system than SSE, but they cannot be used

References

Unite.ai — BootLoops 1.0 coverage unite.ai

The initial 1.0 release documented 30 end-to-end integral computations: 15 were reproductions of known literature, while the remaining 15 represented novel results, including challenging elliptic Feynman integrals that were previously uncomputed.

r/singularity discussion thread reddit.com

Critical review of Schwartz’s AI-assisted preprints revealed technical sloppiness, most notably hallucinated citations where AI-generated manuscripts listed spurious authors for real paper titles.

NBER WP 35782 (Andrews, Schwartz, Shapiro) nber.org

The workflow flagged discrepancies in 3,460 articles or their associated appendices… In nearly 1,000 cases, the system autonomously proposed extensions to the original research.

Marginal Revolution — Tyler Cowen marginalrevolution.com

Experts like Per Engzell have expressed skepticism regarding the AI’s ‘improvement’ claims, questioning whether original authors would agree with the AI’s re-implementations.

arXiv 2512.07809 — Jonathan Oppenheim arxiv.org

The AI-generated criterion tested ‘locality of the Hamiltonian’ rather than the intended property of nonlinearity, making the entire result irrelevant to the problem it claimed to solve… a ‘consensus of hallucinations’.

Aspen Center for Physics — ‘10,000 Einsteins’ panel aspenphys.org

Some physicists argue that if AI can solve second-year doctoral problems in minutes, the traditional educational model is ‘collectively broken’… AI is proposed not as an agent to push the ultimate frontier further, but as a tool to fill in these gaps.

The Moonlight review of ‘Learning to Discover Interesting Mathematics’ themoonlight.io

Experts have questioned whether the interestingness ranking will remain stable as a library grows and premises multiply, potentially changing the ‘conditional’ difficulty of previously discovered theorems.

alphaXiv discussion page for arXiv:2609.28603 alphaxiv.org

Critiques on alphaXiv note that proof length can be stylistic and that it remains to be seen whether these autonomously generated libraries will lead to breakthroughs in human-relevant fields like physics or economics.

ProofOptimizer (arXiv:2510.15700, ICLR 2026) arxiv.org

ProofOptimizer can reduce the length of proofs generated by state-of-the-art provers (such as Seed-Prover) by 87% on miniF2F, 57% on PutnamBench, and roughly 50% for IMO 2025 problems.

STP: Self-play Theorem Prover (arXiv:2505.22257) arxiv.org

The STP framework explicitly extends Minimo’s conjecturer-prover idea to practical languages like Lean 4, using DeepSeek-Prover-V1.5 as its base… generating millions of conjectures and proving them to create an adaptive curriculum.

Kevin Buzzard, Xena Project blog (Oct 2026) xenaproject.wordpress.com

If a proof remains too complex for human absorption, it fails the primary goal of mathematics: advancing understanding rather than just confirming truth.

The Guardian, ‘OpenAI mathematicians millennium prize problem’ (Sep 2026) theguardian.com

Mathematicians like James Maynard and Javier Gómez-Serrano have noted that while AI results often pass formal machine verification in Lean, they frequently lack ‘storytelling’ or conceptual clarity.

Elhage et al., ‘Toy Models of Superposition’ (Anthropic, arXiv:2209.10652) arxiv.org

Neural networks can represent more features than they have dimensions by storing them as nearly-orthogonal directions, a phenomenon we call superposition.

Emergent Mind — ‘Fully disentangle constituent streams from mixed hidden state’ (open problem) emergentmind.com

A substantial residual gap remains between mixed-stream decoding and single-stream baselines; fully recovering constituent continuations from a superposed state is an unresolved challenge.

aimodeling.com — ‘LLM Linear Superposition: Two Thoughts’ aimodeling.com

Superposition fidelity is maximized at initialization and gradually diminishes as the model optimizes the standard language modeling objective — the training process actually degrades the model’s natural ability to maintain distinct semantic signals.

The Sequence (Substack) — mechanistic interpretability edition thesequence.substack.com

Superposition drives polysemanticity, where individual neurons fire for multiple unrelated concepts — encouraging superposition at inference time may trade off against interpretability and safety audits that rely on monosemantic features.

aimodels.fyi — independent paper summary aimodels.fyi

Fine-tuning the model to improve superposition linearity results in a measurable decrease in single-stream quality — Pythia-2.8B LAMBADA accuracy dropped from 0.544 to 0.357 after the linearity-restoring procedure.

aicoder.com — practitioner commentary on the Tikhonov et al. result aicoder.com

The ‘guided decoding’ trick is not yet a shipped system; it fails to provide latency benefits for single requests and loses efficiency if the two streams differ significantly in length.

Sources

References

Jack Sun, writing.