All entries

The Association of Transformer-based Sentiment Analysis with Symptom Distress and Deterioration in Routine Psychotherapy Care

arXiv:2605.09838v1 Announce Type: new Abstract: Sentiment analysis has been of long-standing interest in psychotherapy research. Recently, the Transformer deep learning architecture has produced text-

researcharxiv-cs-cl

Research

The Astonishing Ability of Large Language Models to Parse Jabberwockified Language

DGX agent

arXiv:2602.23928v2 Announce Type: replace Abstract: We show that large language models (LLMs) have an astonishing ability to recover meaning from severely degraded English texts. Texts in which conten

researcharxiv-cs-cl

12 May 2026

The Attacker in the Mirror: Breaking Self-Consistency in Safety via Anchored Bipolicy Self-Play

arXiv:2605.08427v1 Announce Type: new Abstract: Self-play red team is an established approach to improving AI safety in which different instances of the same model play attacker and defender roles in

The autoPET3 Challenge: Automated Lesion Segmentation in Whole-Body PET/CT nicode{x2013} Multitracer Multicenter Generalization

arXiv:2605.05775v2 Announce Type: replace-cross Abstract: We report the design and results of the third autoPET challenge (MICCAI 2024), which benchmarked automated lesion segmentation in whole-body P

Hardware

The benchmarks show the gap. NVLS all-reduce latency drops from 586.1µs on H200 to 313.3µs on GB200. In MoE prefill at EP=4, combine falls f…

The benchmarks show the gap. NVLS all-reduce latency drops from 586.1µs on H200 to 313.3µs on GB200. In MoE prefill at EP=4, combine falls from 730.1µs to 438.5µs. For decode, GB200 sustains much high

hardwareperplexity--x

The Benefits of Temporal Correlations: SGD Learns k-Juntas from Random Walks Efficiently

arXiv:2605.10237v1 Announce Type: new Abstract: We study how temporal correlations in the data can make certain sparse learning problems efficiently learnable by gradient-based methods. Our focus is o

The Bystander Effect in Multi-Agent Reasoning: Quantifying Cognitive Loafing in Collaborative Interactions

arXiv:2605.10698v1 Announce Type: cross Abstract: Multi-agent systems (MAS) assume that collaborating inherently improves Large Language Model (LLM) reasoning. We challenge this by demonstrating that

The Cancellation Hypothesis in Critic-Free RL: From Outcome Rewards to Token Credits

arXiv:2605.08666v1 Announce Type: new Abstract: A commonly accepted explanation of critic-free RL for LLMs, based on sequence-level rewards, is that it reinforces successful rollouts with a positive a

The Cartesian Shortcut: Re-evaluate Vision Reasoning in Polar Coordinate Space

arXiv:2605.09883v1 Announce Type: cross Abstract: As current Multimodal Large Language Models rapidly saturate canonical visual reasoning benchmarks, a key question emerges: do these strong scores gen

Industry

The Convergence of Open Table Formats and Open Catalogs: Catalog Commits is Generally Available

Databricks announced the general availability of Catalog Commits, a feature that integrates open table formats with open catalog systems to enable versioning and tracking of catalog-level changes. Thi

industrydatabricks

The Differences Between Direct Alignment Algorithms are a Blur

model-releasesarxiv-cs-lg

arXiv:2502.01237v3 Announce Type: replace Abstract: Direct Alignment Algorithms (DAAs) simplify LLM alignment by directly optimizing policies, bypassing reward modeling and RL. While DAAs differ in th

The Direct Integration Theorem: A Rigorous Framework for Consistent Discrete Solutions of the Inverse Radon Problem

arXiv:2605.09020v1 Announce Type: new Abstract: This paper presents a novel Direct Integration Theorem (DIT), derived as a non-trivial corollary of the classical Central Slice Theorem (CST). The DIT p

researcharxiv-cs-cv

The Download: a Nobel winner on AI, and the case for fixing everything

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Three things in AI to watch, according to a Nobel-winning econ

researchmit-tech-review

The DSA's Blind Spot: Algorithmic Audit of Advertising and Minor Profiling on TikTok

arXiv:2603.05653v2 Announce Type: replace-cross Abstract: Adolescents spend an increasing amount of their time in digital environments where their still-developing cognitive capacities leave them unab

The Echo Amplifies the Knowledge: Somatic Marker Analogues in Language Models via Emotion Vector Re-Injection

arXiv:2605.08611v1 Announce Type: new Abstract: Current language model memory systems store what happened but not how it felt. This distinction -- between semantic memory (knowing about a past event)

Hardware

The EDA Primer: From RTL to Silicon

The EDA Primer covers the semiconductor design and manufacturing workflow, explaining how Electronic Design Automation tools transform Register Transfer Level (RTL) code into physical silicon through

hardwaresemianalysis

The Ensemble Schr{odinger Bridge filter for Nonlinear Data Assimilation

arXiv:2512.18928v3 Announce Type: replace Abstract: This work introduces a novel nonlinear optimal filtering method, termed the Ensemble Schr{odinger Bridge nonlinear filter. The proposed filter combi

The Extrapolation Cliff in On-Policy Distillation of Near-Deterministic Structured Outputs

model-releasesarxiv-cs-cl

arXiv:2605.08737v1 Announce Type: cross Abstract: On-policy distillation (OPD) is widely used for LLM post-training. When pushed with a reward-extrapolation coefficient lambda > 1, the student can lif

Tutorials

The finite expression method for turbulent dynamics with high-order moment recovery

arXiv:2605.10687v1 Announce Type: new Abstract: Turbulent dynamical systems are characterized by nonlinear interactions and stochastic effects that generate coupled statistical quantities, such as non

tutorialsarxiv-cs-lg

Agents

The First Drop of Ink: Nonlinear Impact of Misleading Information in Long-Context Reasoning

arXiv:2605.10828v1 Announce Type: new Abstract: As large language models are increasingly deployed in retrieval-augmented generation and agentic systems that accumulate extensive context, understandin

agentsarxiv-cs-ai

The FT says that Amazon employees are doing random unnecessary task automations to consume tokens and to show their bosses that they're usin…

The FT says that Amazon employees are doing random unnecessary task automations to consume tokens and to show their bosses that they're using AI more https://www.ft.com/content/8ee0d3ef-9548-422d-8ff1

safetygary-marcus--x

The Generalized Turing Test: A Foundation for Comparing Intelligence

arXiv:2605.10851v1 Announce Type: new Abstract: We introduce the Generalized Turing Test (GTT), a formal framework for comparing the capabilities of arbitrary agents via indistinguishability. For agen

The Geometric Reasoner: Manifold-Informed Latent Foresight Search for Long-Context Reasoning

arXiv:2601.18832v3 Announce Type: replace-cross Abstract: Scaling test-time compute enhances long chain-of-thought (CoT) reasoning, yet existing approaches face a fundamental trade-off between computa

The Geometric Structure of Models Learning Sparse Data

arXiv:2605.08464v1 Announce Type: new Abstract: The manifold hypothesis (MH) is often used to explain how machine learning can overcome the curse of dimensionality. However, the MH is only applicable

safetyarxiv-cs-lg

The Geometric Wall: Manifold Structure Predicts Layerwise Sparse Autoencoder Scaling Laws

arXiv:2605.09887v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) operationalise the linear representation hypothesis: they reconstruct model activations as sparse linear combinations of in

The Geometry of Forgetting: Temporal Knowledge Drift as an Independent Axis in LLM Representations

arXiv:2605.09195v1 Announce Type: new Abstract: Large language models confidently produce outdated answers, and no existing method can detect them. We show this is not an engineering failure but a str

The Global Empirical NTK: Self-Referential Bias and Dimensionality of Gradient Descent Learning

model-releasesarxiv-cs-lg

arXiv:2605.08746v1 Announce Type: new Abstract: In training a neural network with gradient descent (GD), each iteration induces a linear operator that governs first-order updates to a model's internal

The Gordian Knot for VLMs: Diagrammatic Knot Reasoning as a Hard Benchmark

arXiv:2605.09900v1 Announce Type: new Abstract: A vision-language model can look at a knot diagram and report what it sees, yet fail to act on that structure. KnotBench pairs an 858,318-image corpus f

The Grounding Gap: How LLMs Anchor the Meaning of Abstract Concepts Differently from Humans

arXiv:2605.08837v1 Announce Type: cross Abstract: Abstract concepts - justice, theory, availability - have no single perceivable referent; in the human brain, their meaning emerges from a web of exper

The Impact of Editorial Intervention on Detecting Native Language Traces

arXiv:2605.10216v1 Announce Type: new Abstract: Native Language Identification (NLI) is the task of determining an author's native language (L1) from their non-native writings. With the advent of huma

researcharxiv-cs-cl

The Invisible Handshake: Persistent Overpricing by Adaptive Market Agents

arXiv:2510.15995v3 Announce Type: replace-cross Abstract: We study overpricing in a repeated game between two representative agents: a market maker, who controls market liquidity, and a market taker,

The Last Word Often Wins: A Format Confound in Chain-of-Thought Corruption Studies

arXiv:2605.10799v1 Announce Type: cross Abstract: Corruption studies, the primary tool for evaluating chain-of-thought (CoT) faithfulness, identify which chain positions are 'computationally important

The Metacognitive Probe: Five Behavioural Calibration Diagnostics for LLMs

arXiv:2605.09844v1 Announce Type: new Abstract: The Metacognitive Probe is an exploratory five-task, 15-slot diagnostic that decomposes an LLM's confidence behaviour into five behaviourally-distinct d

Agents

the Mini Shai-Hulud attack is scary because it attacks new AI coding workflows like CI, editor hooks, agent configs, etc

The Mini Shai-Hulud attack targets emerging AI-assisted development workflows by compromising multiple integration points including continuous integration systems, code editor hooks, and AI agent conf

agentsyohei-nakajima--x

The Mixing method: low-rank coordinate descent for semidefinite programming with diagonal constraints

arXiv:1706.00476v4 Announce Type: replace-cross Abstract: In this paper, we propose a low-rank coordinate descent approach to structured semidefinite programming with diagonal constraints. The approac

Tutorials

The new Cofounder site is literally a step-by-step guide on how to start a company. When i started my first company there was all sorts of s…

tutorialsyohei-nakajima--x

The new Cofounder site is literally a step-by-step guide on how to start a company. When i started my first company there was all sorts of stuff i had to learn and all of the guides were SEO maxxed sl

Industry

The newest AI boom pitch: Host a mini data center at your home

Span, Nvidia, and PulteGroup are testing home-based data center nodes that use unused grid capacity to cut costs, ease community pushback, and support growing AI demand. The Span installation bundles

industryars-technica

The Observable Wasserstein Distance

arXiv:2605.09916v1 Announce Type: cross Abstract: We introduce the observable Wasserstein distance, a framework for deriving lower bounds on the Wasserstein distance between probability measures on Po

The Open-Box Fallacy: Why AI Deployment Needs a Calibrated Verification Regime

arXiv:2605.10601v1 Announce Type: new Abstract: AI deployment in sensitive domains such as health care, credit, employment, and criminal justice is often treated as unsafe to authorize until model int

The Pokemon Theorem and other Fairness Impossibility Results

arXiv:2605.09221v1 Announce Type: cross Abstract: Fairness impossibility results often look like distinct scalar incompatibility statements. We show that several share one RKHS geometry: fairness crit

The Polynomial Counting Capabilities of Message Passing Neural Networks

arXiv:2605.10393v1 Announce Type: new Abstract: The counting power of Message Passing Neural Networks (MPNN) has been the subject of many recent papers, showing that they can express logic that involv

The Power of Second Order Methods for Sequence Preconditioning

arXiv:2605.08390v1 Announce Type: new Abstract: Sequence prediction methods for dynamical systems with long memory, i.e. marginally stable systems, typically achieve regret that grows polynomially wit

The Procrustean Bed of Time Series: The Optimization Bias in Point-wise Loss Functions

arXiv:2512.18610v3 Announce Type: replace Abstract: Intuitively, a more deterministic time series should be easier to forecast. However, point-wise loss functions (e.g., MSE and MAE), serving as diffe

safetyarxiv-cs-lg

The Propagation Field: A Geometric Substrate Theory of Deep Learning

arXiv:2605.08529v1 Announce Type: new Abstract: Modern deep learning treats neural networks primarily as endpoint functions from inputs to outputs. Inspired by the shift from force to geometry in phys

The Realignment Problem: When Right becomes Wrong in LLMs

model-releasesarxiv-cs-cl

arXiv:2511.02623v2 Announce Type: replace Abstract: Post-training alignment of large language models (LLMs) relies on large-scale human annotations guided by policy specifications that change over tim

Agents

The Reciprocity Gradient

arXiv:2605.08323v1 Announce Type: cross Abstract: Communication is fundamental to sustaining reciprocity and cooperation in strategic interactions. We identify and formulate the influence attribution

agentsarxiv-cs-ai

Industry

The Rise of Sports Intelligence: How the Lakehouse Turns Tracking Data into Competitive Advantage

Databricks explores how lakehouse architecture enables sports organizations to process and analyze player tracking data at scale, converting raw motion capture and sensor information into actionable c

industrydatabricks

The Safety-Aware Denoiser for Text Diffusion Models

arXiv:2605.08116v1 Announce Type: cross Abstract: Recent work on text diffusion models offers a promising alternative to autoregressive generation, but controlling their safety remains underexplored.