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ProtoEEG: Structured Prototype Alignment for EEG Visual Decoding

arXiv:2610.33065v1 Announce Type: new Abstract: EEG visual decoding remains difficult because scalp recordings are noisy, spatially coarse, and variable across trials and subjects. Most systems align each trial with a dense image embedding, although that embedding mixes semantic content with instance-specific texture, pose, and background. This target can demand details absent from EEG and provides no explicit account of the visual factors supporting a prediction. We propose \textbf{ProtoEEG}, a prototype-mediated framework that reformulates EEG--image alignment as soft composition over a shared learnable visual codebook. A trainable EEG encoder and a frozen visual teacher produce assignment distributions over the same $K=64$ prototypes. Their weighted reconstructions form a structured rep

A Mathematical Theory of the Jacobian Lens

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

MANTA: Unlocking DAG Flexibility in Asynchronous BFT

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

PRISM: Promoting Sampling Diversity of Large Language Models with Implicit Stochastic Modulation

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

Whispering Wisely: Smart Subset Selection for ASR Fine-Tuning

arXiv:2610.32997v1 Announce Type: new Abstract: Automatic Speech Recognition (ASR) models such as OpenAIs Whisper have substantially improved speech recognition quality but remain expensive to fine-tune, especially when large amounts of transcribed audio are available. This makes it challenging for many practitioners to adapt these models to new languages or domains. In this work, we introduce an embedding-based subset selection approach for selecting smaller training sets for ASR fine-tuning. Using Whispers pretrained encoder, we map each audio sample into a fixed-dimensional embedding space and apply a greedy k-center algorithm to select diverse and representative subsets of the training data. We evaluate this approach on both Hebrew and Danish, showing that k-center selection often impr

OpenESM: Energy-Steered Models Are Scalable Foundation Learners

arXiv:2610.33005v1 Announce Type: new Abstract: Energy-based models have shown promising properties for domain-specific tasks but remain challenging in modeling general-purpose scenarios. To bridge this gap, we design energy-steered models (ESMs) for foundational language modeling. ESMs integrate energy steering and token generation within a single architecture to leverage the energy-guided search to refine token generation. Specifically, ESMs first optimize a latent state over the energy landscape, then leverages the refined latent state for token generation. By performing such energy-guided pre-training on large-scale corpora, ESMs learn generally effective energy landscape where favorable tokens correspond to lower energy values. To explore the scaling behavior of ESMs, we pre-train ESM

A Canary in the Neural Mine: Catching an Image Classifier's Silent, Confident Failures

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

EKT-Router: Expert-Evidence Alignment for Embodied Vision-Language Models

arXiv:2610.33044v1 Announce Type: new Abstract: Embodied vision-language models must answer questions from partial, evolving observations. Their success depends on both the reasoning capability selected and the evidence available to it. Expert routers adapt internal computation, while tool-augmented systems acquire external evidence; choosing these independently can leave a suitable expert without the evidence it needs. We introduce EKT-Router, a bounded framework that jointly selects a capability expert, optional knowledge, and perceptual tools. Outcome-derived signatures represent the overlapping strengths of General, Spatial Reasoning, and Spatiotemporal experts. A joint utility scores complete expert-evidence routes under a call budget. After acquisition, a packet verifier admits valid

ReMedI: Inference-Time Refinement and Self-Distillation for Controllable Molecular Optimization

arXiv:2610.32103v1 Announce Type: new Abstract: In drug development, lead optimization must improve competing properties, such as increasing permeability and reducing toxicity, while limiting scaffold changes that could compromise the lead's potency. RL-based post-training, such as C-MORAL, addresses this problem by fine-tuning molecular language models to directly optimize molecular leads for objectives of interest, which relies on expensive task-specific datasets and faces challenges in achieving multi-objective optimization (MOO). We argue a pre-trained model may already contain useful molecular edits that zero-shot generation cannot efficiently reach. Motivated by the limited search capabilities of zero-shot generation, we develop an efficient Metropolis-Hastings search strategy to bet

Supervision Without Blocking: An Evidence-Driven Redesign of OTP-Style Fault Tolerance for Cooperative Runtimes

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

Linear-Time Context Compression with State-Space Encoding

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

Intuition with Evidence: Adaptive Dual Memory for Multimodal Agents

arXiv:2610.33019v1 Announce Type: new Abstract: As multimodal agents engage in sustained interactions, long-term memory serves two distinct but complementary functions: fine-grained evidence recall and internalized interpretation for global understanding. Although episodic and latent memory address these demands through explicit historical records and internalized experience, neither paradigm alone can accommodate the diverse memory affinities across tasks, owing to limitations in episodic evidence coverage and integration, and in latent factual grounding. To bridge this divide, we propose a dual-memory framework, unifying a model-native latent memory as an intuitive prior with a multi-indexed episodic memory for exact evidence reconstruction via retrieval-conditioned joint alignment. At i

Compatible Likelihoods for Flow Matching on Manifolds

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

From Writing Advice to Manuscript Governance: A Constrained-Transformation Framework for Academic Revision

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

Putting Transformer Theories to the Test

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

OpenAI announces three big science discoveries and a new way to work with academics

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 […]

Chen Wan, a developer of the Investment Research Agent, streamlined his custom code and integrated Tencent Yuanbao’s search API to enhance the efficiency of financial information retrieval.

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.

Import AI 478: Math for alignment; ProgramBench; and Mirendil shows what RSI means

What is “night” to a machine?

Cognitively Constrained Partners for Zero-Shot Coordination in Real-Time Partitioned Control

arXiv:2610.31726v1 Announce Type: new Abstract: Effective human-machine teaming requires agents that coordinate with unpredictable human partners. Reinforcement learning from live human feedback or large behavioural datasets is expensive, while purely simulated training overfits to artificial conventions and fails at Zero-Shot Coordination with real people. We propose a framework for training teaming agents against Cognitively Constrained Partners: simulated partners whose diversity emerges from procedural constraints grounded in human physiological literature, specifically reaction delays and perceptual limitations, rather than from reward shaping or historical training checkpoints. In a modified Lunar Lander environment, we report preliminary evidence that these partners play more like r

New research finds: The existing critic in reinforcement learning can be directly repurposed as a confidence estimator for large language models.

An AI research paper published on October 26, 2026, on arXiv (ID 2610.31820v1) states that, for large language models (LLMs) trained using value-based reinforcement learning (value-based RL), their existing critic modules can serve as high-accuracy, highly calibrated confidence estimators without requiring additional training or data.

Why it matters：This study reveals the mathematical equivalence between confidence estimation and Critic training in reinforcement learning (under a well-defined correctness metric), and empirically demonstrates the effectiveness and generalization capability of reusing the Critic across more than ten domains, including mathematics, code generation, tool invocation, and general reasoning. These findings hold direct engineering value for lowering the barriers to trustworthy LLM deployment.