When Correctness Feedback Erases Evidence: Phase-Separated Completeness Auditing in Verus
arXiv:2610.20676v1 Announce Type: new
Abstract: Correctness feedback suppresses the very evidence that specification-completeness auditing is designed to uncover: programs that satisfy a written contract but violate intended behavior. We demonstrate this objective reversal on 12 Verus/Rust tasks ac...
Expanding Attention: A Taylor-Series Route to Linear-Time Transformers with a Fixed Number of Recurrent States
arXiv:2610.20637v1 Announce Type: new
Abstract: We expand the exponential kernel of attention into a truncated Taylor series of $n$ terms and factorize each term into a tensor-power inner product, which collapses the causal history into exactly $n$ recurrent states, each updated by one rank-one out...
Proof-based general-purpose computing with Magnetar
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security.
The post Proof-based general-purpose computing ...
Smart Alone, Blind Together: Why Accuracy Fails to CertifyTheory of Mind in Multi-Agent Teams
arXiv:2610.19051v1 Announce Type: new
Abstract: Coordinators that act on what they infer about teammates are usually certified by the accuracy of those inferences. We argue that accuracy is the wrong certificate, for two reasons. First, the accuracy needed to act depends on what a wrong action cost...
Beyond Equation Search: Conjecture-Guided Discovery with SCOPE and ClosureBench
arXiv:2610.18938v1 Announce Type: new
Abstract: Most symbolic regression methods optimize candidate equations, but improving an equation's fit does not establish whether its underlying scientific conjecture should be retained, revised, or rejected. A conjecture may admit multiple mathematical reali...
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-student disagreement, and mismatch direction. We instead derive token importance from the distillation ...
Plan or Retrieve First? A Scale Study of Tool Discovery Architectures
Tool-augmented LLM agents discover relevant APIs from large registries before planning execution, yet finite context windows prohibit injecting all tool definitions into a single prompt. The dominant approach, which we term Retrieve-then-Plan (RtP), embeds the user query and retrieves similar tools ...
Scientists recreate a bizarre quantum effect that stops a particle cold
Oxford physicists have recreated a bizarre quantum effect in which a particle responds to a magnetic field without ever traveling through it. Using a hybrid quantum computer, they demonstrated how introducing a simulated magnetic flux could completely halt a particle's movement through quantum inter...
Physicists discover “astonishing” property of light that defies expectations
Scientists have uncovered a surprising hidden ability of light that allows it to develop spinning, twisting behavior as it travels through empty space, without special lenses, mirrors, or materials. Researchers discovered that light’s internal geometry can naturally produce left- and right-handed pa...
Behind-the-Meter Photovoltaic Visibility from Net Smart Meters: Deployment Regimes and Operational Insights
arXiv:2610.12527v1 Announce Type: new
Abstract: Distribution operators need visibility of behind-the-meter photovoltaic (PV) generation, yet most residential customers are observed only through net smart meters. Deep-learning-based PV disaggregation can restore this visibility, but its operational ...
Equal-Budget Monte Carlo Tree Search versus Randomized-Restart List Scheduling for DAG Workflows: A Controlled Equal-Budget Computational Study
arXiv:2610.12542v1 Announce Type: new
Abstract: Monte Carlo Tree Search (MCTS) is often proposed for scheduling directed-acyclic-graph (DAG) workflows, yet most studies never run the comparison that could refute it - giving a much simpler method the same search effort. We run that comparison under ...
Why enterprise agentic AI needs an evidence standard.
Usage metrics show who logged in. Neetu Yadav sets out a five-part evidence standard covering reach, depth of use, quality, risk, and business impact, so enterprise leaders can scale agentic AI with confidence.