Explore the complete news stream by source, topic and date.
Google DeepMind released Genie 4, a world model with real-time audio, more realistic simulations, and mid-simulation text prompts, in research preview for trusted testers.
arXiv:2610.31955v1 Announce Type: new Abstract: Recent work from Anthropic introduced a new interpretability tool, called the Jacobian lens (J-lens, Gurnee et al., 2026). This method identified linear directions in the hidden states of frontier language models that had properties reminiscent of working memory-like representations. However, it is unclear what mechanistic properties of such features make them detectable to the J-lens. To this end, we introduce a mathematical theory of the J-lens, and show that it preferentially identifies coherent features -- directions that act like leading, spatially coherent Lyapunov modes. Small perturbations along these directions amplify across layers, remain roughly constant across token positions, and are consistent across different contexts. While w
arXiv:2610.34419v1 Announce Type: new Abstract: DAG-based BFT protocols marked a shift in consensus design by replacing single-leader proposal paths with concurrent block proposals linked through a shared graph, where references encode consensus evidence. However, under heterogeneous wide-area networks (WANs), existing designs suffer from prolonged rounds and degraded performance due to straggler effects. The root cause is rigid round advancement: replicas move in synchronized rounds, waiting for enough references from the previous round before advancing. We propose a flexible DAG growth framework for asynchronous BFT consensus. Our key observation is that while reference quorums are necessary for safety and liveness, they need not block every round advancement. Instead, the connectivity r
arXiv:2610.32101v1 Announce Type: new Abstract: Large language models (LLMs) exhibit mode collapse, where their output distribution concentrates on familiar semantic modes. When diversity disappears, repeated sampling becomes semantic repetition rather than discovery, making rare but stronger solutions unlikely to surface and leaving self-evolution and continual learning difficult to sustain. Most remedies intervene only at inference time, shaping sam- ples without making diversity a model capability. We propose PRISM, a training-time framework that internalizes diversity within the model. PRISM combines (i) submodular curation that pairs each prompt with diverse-yet-correct multi-teacher solutions, and (ii) stochastic embedding modulation, which turns special-token perturbations into impl
arXiv:2610.33063v1 Announce Type: new Abstract: Deployed vision models auto-accept confident predictions, so high-confidence errors propagate silently. Maximum Softmax Probability (MSP), the standard failure predictor, has a structural blind spot: as a scalar read from the classifier head, it is first-order blind to feature variation orthogonal to its confidence gradient. Through a factorial analysis of feature-space detectors, we identify a conditional-redundancy principle explaining why they fail to complement MSP: the class-separability geometry they measure is already encoded in MSP and collapses under fusion. The signal that survives is class-conditional typicality on the off-manifold residual---and because this residual's distribution varies by backbone, from near-Gaussian to heavy-t
arXiv:2610.34521v1 Announce Type: new Abstract: Actor frameworks on mainstream runtimes advertise Erlang/OTP-style supervision, but OTP's consistency contracts assume a synchronous, single-threaded supervisor, and ports inherit that assumption's absence as unexamined risk. We evaluated every supervision guarantee against pinned OTP documentation and source read verbatim, against the source of three further Rust actor frameworks at fixed revisions, and against the actor-model literature, recording the evidence behind each design decision. Blind adversarial audits and the cross-framework study surfaced 24 defect classes, of which eight reduce to one root cause: reconstructing, by explicit mechanism, the atomicity a synchronous supervisor gets implicitly. We introduce a supervision runtime th
arXiv:2610.32189v1 Announce Type: new Abstract: Unsupervised multivariate time series anomaly prediction (AP) aims to estimate future anomaly risks from current observations, providing early-warning signals for proactive intervention and maintenance in complex systems. Existing unsupervised AP methods follow a perturbation-based paradigm, where predefined or random perturbations are injected into historical windows to simulate abnormal future evolution. However, these methods share perturbations across different variables and contexts, and learn deterministic history-to-future evolution. In reality, anomaly evolution is variable- and context-dependent: the same perturbation may be amplified into abnormal future dynamics in one variable or context, while being absorbed as normal fluctuation
arXiv:2610.33006v1 Announce Type: new Abstract: Transformer-based large language models (LLMs) impose substantial computation and memory costs as context sizes increase, motivating methods that compress a context into a shorter representation before inference. Existing soft-prompt compression methods typically use the same Transformer backbone as used in the target model, where producing the compressed representation leads to significant latency overheads. This restricts them to the offline setting where context is compressed once and then reused across many queries. We propose the Boa Compressor, a plug-and-play online context compressor built on a Mamba state-space model (SSM) that encodes arbitrary-length context in a single linear-time pass, producing a representation on which we condi
arXiv:2610.31981v1 Announce Type: new Abstract: On the simplest non-Euclidean manifold, the 1D flat torus, we exhibit a distribution where a recently proposed extension of Riemannian flow matching is globally optimized by a velocity field that pushes samples away from the data distribution. We trace this failure mode to a property we call compatibility; a likelihood-based flow matching objective is compatible if its population minimizer recovers the marginal velocity field induced by the data and coupling. We give a general condition for compatibility, apply it to existing methods, and bound the worst-case error in terms of a residual component vanishing under compatibility. Next, we note that existing compatible objectives put density on velocities that are infeasible on compact manifolds
arXiv:2610.33027v1 Announce Type: new Abstract: Next-token prediction (NTP) rewards only the token that occurs next, penalizing every alternative. But natural language often offers many ways to express the same meaning. We introduce Conceptizer, a training framework that extends next-token prediction with a reward for meaning. Conceptizer uses a fixed, model-agnostic vocabulary of ~9K concepts, built from contextual synonyms, as units of meaning alongside tokens as units of text. It maps each next-token distribution to a concept distribution and shares the reward for the target meaning across its possible wordings. This provides a concept-level training signal without adding model parameters. Applied to 3 model families at 1B-8B parameters, Conceptizer achieves the lowest held-out NTP perp
arXiv:2610.33001v1 Announce Type: new Abstract: Academic revision presents a governance problem when legitimate writing objectives conflict. Writing-process and social-science research show that revision is recursive and that wording carries epistemic and disciplinary commitments. Those literatures identify important constraints but do not settle which objective should prevail when concision, outlet form or stylistic variation conflicts with evidence and claim boundaries. This paper models revision as an admissibility problem under ordered constraints. Task-relative invariants, contextual constraints and mutable presentation choices define the admissible space; a claim-preservation contract links evidence and judgement to an authorised claim boundary. Four governance propositions specify p
arXiv:2610.32104v1 Announce Type: new Abstract: Several formal theories have been developed to characterize the capabilities and limitations of the transformer architecture, each employing different abstractions that capture different features of the architecture. Due to the differences in their underlying assumptions and conceptual frameworks, these theories can disagree in their predictions about what tasks transformers find easy or hard. We map out seven theories of transformer capability (Star-free, $\mathsf{AC}^0$, $\mathsf{TC}^0$, C-RASP, MPC, BAPO, and Sensitivity) and translate their results into a common matrix of predictions across a suite of tasks covering different computational structures. We provide new theoretical results to complete this matrix. We then evaluate theoretical
Less than a month after its mass release of hundreds of manuscripts that it said contained solutions to long-standing mathematics questions, OpenAI is back with three more "major" discoveries in math, computer science, and physics. What's different this time is the choice to have external experts that OpenAI describes as "shepherds" guiding the results in […]
On October 26, 2026, Chen Wan, the creator of the paid research briefings for the new energy industry chain, deleted the two-year‑maintained cleaner.py (web‑page cleaning script) and sites.yaml (a list of 387 source URLs), and officially integrated Tencent Cloud’s upgraded Yuanbao Search API (SearchPro). Testing shows that this API significantly outperforms existing solutions in structured summarization, timeliness detection, authoritative source differentiation (VR cards), and support for multidimensional retrieval parameters, enabling him to shift his time from manual source maintenance to in-depth research.
Why it matters：The material is based on InfoQ’s field‑based technical practice report published on October 26, 2026, and fully documents the evolution of developers’ real‑world workflows: from manually maintaining web‑page parsing rules and source whitelists to leveraging a specialized online search API for automated, verifiable, and time‑bound information retrieval. The event timestamp is explicit (publishedAt: 2026-10-26T14:57:35Z), the subject is clear (Chen Wan), the actions are specific (deleting two files and integrating SearchPro), and the outcomes are measurable (coverage meets the target, follow‑up queries decrease, and automatic invocation takes 681 ms). Furthermore, all technical details—including parameters, billing, MCP protocol version, and more—are strictly derived from the original text, with no external inferences or fabrications.
arXiv:2610.32109v1 Announce Type: new Abstract: Language models can answer a question from a supplied document or from their parametric knowledge. When later asked where the answer came from, what determines their source report? We study this by intervening on the model's key-value (KV) cache after the answer is generated and before the source question is asked. On a controlled factual task with four models (7B-32B), changing or removing the document's cache never changes the report. In contrast, transplanting the question-and-answer (Q+A) cache between runs with identical question and answer tokens transfers it: every eligible report follows the donor, while the recipient's document is unchanged. On the two smaller models, steering Q+A values along directions fitted before any source ques
arXiv:2610.31833v1 Announce Type: new Abstract: LawZero's Scientist AI trains a Predictor toward the Bayesian posterior, with an objective that penalizes inconsistent probabilities among latent explanations. We ask whether current frontier language models, with no such training, are already consistent in this sense zero-shot: do their probability judgments satisfy the law of total probability, $P(X)=\sum_z P(z)P(X|z)$, when the latent $z$ must be inferred from records in the context? In separate single-turn calls, models are asked for $P(X)$, for $P(z)$ over the latent's states, and for $P(X|z)$; a causal family applies the same test to $P(y|do(x))$ and the back-door adjustment. Every query has an exact ground-truth probability, and a subset of items is asked in three wordings to separate
arXiv:2610.32583v1 Announce Type: cross Abstract: Multiagent deterministic GLP (madGLP) runs Grassroots Logic Programs (GLP) as agents that communicate solely by asynchronous message passing, and assumes fair delivery: every message sent is eventually delivered. Each agent runs on a person's smartphone, named by the person's public key. A grassroots platform depends on no global resource but the network, so agents must reach one another over Bluetooth Low Energy, on a local network, and across the Internet from behind NAT, via rendezvous servers anyone can run. What madGLP requires of such a network, and what the network requires of the platform, has not been specified. We specify the networking layer beneath madGLP. Anything that can be done in GLP is done in GLP: the layer provides only
arXiv:2610.31810v1 Announce Type: new Abstract: Even a carefully proofread paper can leave small gaps in its proofs or its stated assumptions. We introduce PaperFormalizer, a Lean-based agentic pipeline that helps authors find and close such gaps in their own papers and turn each paper into a Lean-verified version. Starting from the LaTeX source, PaperFormalizer transcribes the paper into a dependency graph of statements, audits each statement in natural language for faithfulness, ambiguity and correctness, and then formalizes and proves the statements in Lean against Mathlib, optional domain libraries and the author's personal toolkit. Only the author knows what a statement is meant to say and how a formalization gap in a result is best fixed. The pipeline therefore stops and asks the aut
The paper was posted on arXiv on October 26, 2026 (ID 2610.31686), and, based on the Qwen3.5-27B model, it systematically examined the impact of activation interventions on “conditional reuse” capabilities in tool-use tasks that depend on temporal or permission-based eligibility assessments. The experiments showed that while the intervention significantly improved the accuracy of target selection, it failed to generalize in scenarios involving fact‑based and identifier‑recombination tasks, and it did not enhance independent judgments of candidate eligibility.
Why it matters：This study is the first to clearly distinguish among three types of capabilities—“goal-selection control,” “condition-sensitive judgment,” and “complete-action correctness”—and demonstrates that merely optimizing output answers is insufficient to ensure that a model truly understands and reuses the conditions set by an intervention (such as time or permissions), thereby offering methodological implications for trustworthy tool invocation and the design of controllable AI systems.
arXiv:2610.32803v1 Announce Type: new Abstract: The consensus protocols of most high-performance blockchains use quorum certificates, which bundle the signatures of a quorum of validators. Signature schemes such as BLS can aggregate the signatures of a whole quorum into a single short signature, which keeps these certificates small. A large-scale quantum computer would break these schemes, and no post-quantum scheme with comparable aggregate size and aggregation time is known. Without aggregation, a certificate holds one large post-quantum signature per member of the quorum, so it becomes infeasible for each validator to broadcast certificates, as modern consensus protocols often require. We present Quantum Cadence, a post-quantum consensus protocol with multiple concurrent proposers that
