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The Singularity Has a Distribution: Exact Timing Laws and Detection Limits for a Stochastic Intelligence Explosion

arXiv:2610.18445v1 Announce Type: new Abstract: Automating AI research creates a positive feedback loop between software capability and research effort. We study a stochastic realisation of this loop

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The Smart Buildings Challenge: Learning Foundation Models for Real-World Optimization at Scale

DGX agent

arXiv:2610.18511v1 Announce Type: new Abstract: Buildings cause nearly 40% of carbon emissions in the US. Of total building emissions, HVAC systems are responsible for approximately 40-60% and account

safetyarxiv-cs-ai

15 Oct 2026

The sudden collapse of the value of CoT (even summarized CoT) in the agentic era changes working with AI. Effectively steering AIs and getti…

The sudden collapse of the value of CoT (even summarized CoT) in the agentic era changes working with AI. Effectively steering AIs and getting any sort of observability now requires you to ask for int

agentsethan-mollick--x

The Sum of All Summaries: PROPALIGN for Graded Proposition Salience Evaluation

arXiv:2610.19387v1 Announce Type: new Abstract: Identification and ranking of salient propositions in documents is indirectly involved in many AI tasks, but few papers define proposition salience scor

researcharxiv-cs-cl

Theoretical and Practical Advances in Hierarchical Bayesian Deep Learning Methodology

model-releasesarxiv-cs-lg

arXiv:2610.19074v1 Announce Type: new Abstract: Reliable uncertainty quantification remains an important challenge in AI. Hierarchical Bayesian methods provide a principled framework for this by repre

Think in Strategies, Act with Tools: Traceable Agentic Optimization for Combinatorial Problems

arXiv:2610.14736v1 Announce Type: new Abstract: Optimization by PROmpting (OPRO) directly leverages Large Language Models (LLMs) to generate candidate solutions through natural-language prompting. How

agentsarxiv-cs-ai

Thinking Outside the Bots: Human Ideas in AI's Semantic Blind Spots Are Not Inferior

arXiv:2610.17324v1 Announce Type: cross Abstract: AI-generated outputs are frequently perceived as highly creative, but also tend to be more similar to each other (homogenized) than human ideas. Does

ThreadBench: Benchmarking Realism in Agentic Simulation of Real-world Discussions

arXiv:2606.14715v2 Announce Type: replace-cross Abstract: LLM agents are increasingly used to simulate real-world interactions, but it remains unclear whether simulated behaviors preserve the content

Applications

Tiered-Resolution Volumetric Mapping for Real-Time Simultaneous Planning and Mapping on Natural Outdoor Environments

arXiv:2610.20476v1 Announce Type: new Abstract: Real-time volumetric mapping for a planning robot is dominated by the cost of integrating and storing information at the finest resolution, yet only a s

applicationsarxiv-cs-ro

TIMERSI: Towards Self-Improved Agentic Deployment for Time-Series Foundation Models

arXiv:2610.19725v1 Announce Type: new Abstract: Time-series foundation models provide strong zero-shot forecast, but deployment for real- world applications still requires task-specific designs for mo

agentsarxiv-cs-lg

Titan-Llama: Retrofitting Pre-trained LLMs with External Neural Memory Modules

model-releasesarxiv-cs-cl

arXiv:2610.19210v1 Announce Type: new Abstract: Transformers are efficient to pre-train at scale because attention permits parallel computation across sequence positions, but long-context inference re

To Reason but Not to Stop: Understanding and Recovering Pruned Large Reasoning Models

arXiv:2610.19934v1 Announce Type: new Abstract: Weight pruning can degrade the accuracy of large reasoning models (LRMs), yet it remains unclear whether it damages reasoning itself. We find that much

researcharxiv-cs-cl

Token Utility for On-Policy Distillation via Local Trust-Region Optimization

arXiv:2610.18883v1 Announce Type: new Abstract: Token-selection methods for on-policy distillation (OPD) prioritize student-visited positions using diverse signals, including uncertainty, teacher-stud

safetyarxiv-cs-lg

tokenmaxxing: dead and buried.

model-releasesgary-marcus--x

tokenmaxxing: dead and buried. New: Accenture has told employees it will only fund 29 per month of Claude use. Meaning that employees who often spend as much as 1,000 a month are now scrambling to fin

Too Late to Prune: Generative AI, the Screen, and Team Creative Decisions

arXiv:2610.14984v1 Announce Type: cross Abstract: Generative AI makes options cheap and shifts the bottleneck of creative work from generating options to screening them. We argue that AI's value depen

TopicMerge: A Divide and Conquer Approach to Topic Modeling

arXiv:2610.19323v1 Announce Type: new Abstract: Recent advancements in topic modeling have split into two main paths: efficient embedding-based methods that offer scalability but are often outperforme

local-aiarxiv-cs-cl

Topology-Preserving Multi-Usage Pipe Centerline Refinement under Permanent Tubular Occupancy

arXiv:2610.19403v1 Announce Type: new Abstract: Discrete multi-group pipe routers place channels on a grid or coarse graph. The resulting centerlines are topologically feasible but typically contain m

safetyarxiv-cs-ro

TorchSpin: Differentiable Electron Paramagnetic Resonance Simulation in PyTorch

model-releasesarxiv-cs-lg

arXiv:2610.18924v1 Announce Type: cross Abstract: Interpreting electron paramagnetic resonance (EPR) spectra commonly requires repeated forward simulation and optimization of spin Hamiltonian paramete

Toward a Universal Characterization of Sudoku Puzzle Difficulty

arXiv:2507.21137v2 Announce Type: replace Abstract: In this paper we try to answer the question 'What constitutes Sudoku difficulty rating across different Sudoku websites?' Using two distinct methods

Toward Fine-Grained Forgetting:Attribute Unlearning for Multimodal Large Language Models

arXiv:2608.01008v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) can memorize and disclose sensitive information, motivating machine unlearning to remove designated knowled

local-aiarxiv-cs-ai

Toward System One Clinical Intelligence: Parameter-Efficient Fine-Tuning and Counterfactual Explainability of Decision Foundation Models

arXiv:2610.15029v1 Announce Type: cross Abstract: Automated disease classification from patient-reported symptoms requires balancing the speed of lightweight discriminative models against the contextu

Towards a Benchmark for Fire Safety Code Interpretation in the Hong Kong AEC Industry

arXiv:2610.16127v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly explored in the Architecture, Engineering, and Construction (AEC) domain, yet their reliability in regul

Towards a Data-Centric Understanding of Spatial Reasoning in Multimodal Large Language Models

model-releasesarxiv-cs-cl

arXiv:2610.19108v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have been widely applied in embodied agents and robotics for vision tasks. However, MLLMs suffer from limitat

Towards Agentic Robots: Model, Data, Environment, and Harness

arXiv:2610.20442v1 Announce Type: new Abstract: Foundation models are driving a paradigm shift in robotic intelligence, moving beyond low-level action prediction to goal-directed orchestration of data

agentsarxiv-cs-ro

Towards Certifiable State Estimation under Observability Degeneracy

arXiv:2610.20490v1 Announce Type: new Abstract: Certifiable optimization provides global optimality guarantees for non-convex state estimation in robotics. However, under severe physical sensing degen

local-aiarxiv-cs-ro

Towards Efficient Simultaneous Inverse Text Normalization with Pretrained Text-to-Text Language Model and Read-Tag-Write Policy

arXiv:2610.19492v1 Announce Type: new Abstract: Streaming Inverse Text Normalization (ITN) is vital for converting spoken-form outputs from streaming Automatic Speech Recognition into formatted writte

safetyarxiv-cs-cl

Towards Merging-conditional Backdoor Unalignment against Large Language Models

arXiv:2610.14701v1 Announce Type: cross Abstract: Model merging has become widely adopted for enhancing the task-specific performance of Large Language Models (LLMs) without additional training. While

Applications

Towards Resilience: Slice-Driven Orchestration of Mobile IoMT in 6G-Edge-Continuum

arXiv:2610.17296v1 Announce Type: new Abstract: Emerging healthcare services, such as real-time diagnostics, remote health monitoring and computer-assisted surgery, require stringent Quality of Servic

applicationsarxiv-cs-ai

Applications

Towards Training and Inference of Graph Neural Networks in Graph Databases

arXiv:2610.19142v1 Announce Type: new Abstract: Graph Neural Networks on large graphs commonly rely on in-memory graph storage with ad hoc data processing pipelines for both training and inference. Gr

applicationsarxiv-cs-lg

Trace Evolution Bench: Measuring Whether Agents Improve from Their Own Environment Traces

arXiv:2610.15055v1 Announce Type: new Abstract: Self-evolving agents are usually scored by endpoint Gain: held-out success after an update minus success before. That difference can credit a frozen res

Trace-to-Architecture: Capability Bottleneck Diagnosis for Harness Revision

arXiv:2610.16725v1 Announce Type: new Abstract: Improving an LLM agent requires knowing what to change when it fails. Similar failures can require different fixes, and resolving one problem can expose

agentsarxiv-cs-ai

TRACER: Trace-Regularized Training Against Indirect Prompt Injection

arXiv:2610.19133v1 Announce Type: new Abstract: Indirect prompt injection uses untrusted retrieved content, tool outputs, or agent messages to override an LLM's intended behavior. We show that defense

agentsarxiv-cs-lg

Tracing Morphology, Semantics, and Divergence in Language Acquisition

arXiv:2610.19041v1 Announce Type: cross Abstract: Understanding the dynamics of language acquisition in Large Language Models (LLMs) is crucial for uncovering how statistical representations evolve in

researcharxiv-cs-cl

Tracking a Small Set of Experts in Online Convex Optimization

arXiv:2610.20416v1 Announce Type: new Abstract: For the fundamental problem of prediction from expert advice, Freund asked whether one could compete with a comparator that switches k times among only

researcharxiv-cs-lg

Tracking large chemical reaction networks and rare events by neural networks

arXiv:2512.10309v3 Announce Type: replace-cross Abstract: Chemical reaction networks are widely used to model stochastic dynamics in biophysical processes. Their exact probabilistic description via th

researcharxiv-cs-lg

Train to Stitch: Topology-Preserving Regularization for Interoperable Representations

arXiv:2610.19401v1 Announce Type: cross Abstract: As large, powerful pre-trained models become more prevalent, there is growing interest in developing methods that allow different neural networks to b

researcharxiv-cs-cv

Tutorials

Trainable at Depth, Solvable at Small Size: What the Loss Surface of a Polynomial Activation Dictates for Designing, Initializing and Training Networks

arXiv:2610.20069v1 Announce Type: new Abstract: What makes a deep network train, and what shape is its loss surface? We answer both with one construction at two scales: a polynomial activation f (z) =

tutorialsarxiv-cs-lg

Training a Compact, Strong, and Realistic Text-to-Speech Model

arXiv:2610.14776v1 Announce Type: cross Abstract: Text-to-speech (TTS) systems are conventionally optimized for intelligibility and speaker similarity. However, neither objective captures the prosodic

Tutorials

Training Dynamics of Transformers on Unbounded Inputs: Attention Learning Can Lag Output Layer Learning

arXiv:2610.20337v1 Announce Type: new Abstract: The success of transformers is attributed to several factors, including their capacity to express formal languages over variable-length inputs, and abil

tutorialsarxiv-cs-lg

Training Language Models to Fail Gracefully

arXiv:2610.20401v1 Announce Type: new Abstract: Reinforcement learning makes language models better at solving tasks, but the same mechanism also rewards guessing. When success is the only way to earn

safetyarxiv-cs-lg

Training LLMs Beyond Next Token Prediction - Filling the Mutual Information Gap

arXiv:2511.00198v2 Announce Type: replace-cross Abstract: Autoregressive language models are usually trained with next-token prediction under a fixed left-to-right target order. This paper studies tar

Transparency as Answerability: Role-Dependent Information Flow Modeling

arXiv:2610.16887v1 Announce Type: new Abstract: Transparency in AI-supported decision systems is widely required but poorly defined. Existing approaches treat transparency as a property of systems or

Transparent Information Flow Modeling for Role-Aware AI Act Assessment

arXiv:2610.16916v1 Announce Type: new Abstract: The European Union's Artificial Intelligence Act introduces a structured framework for governing high-risk AI systems, emphasizing transparency, risk ma

safetyarxiv-cs-ai

TRAP: Tool-result Replay Attack for Probing Misbinding in LLM Agents

arXiv:2610.16333v1 Announce Type: new Abstract: As AI agents increasingly act through external tools, tool results are no longer simple outputs but execution artifacts that must remain correctly assoc

Tread: A Visuo-Inertial Framework for Terrain and Locomotion Context-Aware Gait-Phase and Speed Estimation in Wearable Robotics

arXiv:2610.19067v1 Announce Type: new Abstract: A wearable assistive robot must continuously estimate terrain, locomotion mode, gait phase, and walking speed. These quantities are commonly addressed b

researcharxiv-cs-ro

Tree-Structured Synergy of Large Language Models and Bayesian Optimization for Efficient CASH

arXiv:2601.12355v3 Announce Type: replace Abstract: To lower the expertise barrier in machine learning, the AutoML community has focused on the CASH problem, which jointly automates algorithm selectio

safetyarxiv-cs-lg

TreeFlex: Agents Navigating Their Own Search Trees

model-releasesarxiv-cs-cl

arXiv:2610.20334v1 Announce Type: cross Abstract: A ReAct-style agent accumulates observations, intermediate results, and failed attempts in a single context. As this context grows, stale and off-path

TreeSpark: Learning to Draft the Trees You Verify for Speculative Decoding

arXiv:2610.20175v1 Announce Type: new Abstract: Parallel drafters such as DFlash and DSpark reduce proposal latency, but early mismatches limit how much of each block can be accepted. Retaining altern

local-aiarxiv-cs-cl