Browse records
Every public record, newest first — papers, preprints, datasets, theses, reports and software, all free to read. You can also browse the complete archive by date.
Filter and sort
181 records found · page 6 of 10
-
Dataset
Court Rules: U.S. federal court rules, judge standing orders and filing deadlines dataset
Court Rules is a free reference for U.S. federal court practice: the Federal Rules of Civil Procedure, district local rules, individual judge standing orders and court holidays, organized by court. The deadline calculator counts calendar, business and court days and applies each…
-
Preprint
S H N O U D I R E S E A R C H On the Putative Extremal [72, 36, 16] Binary Code
Whether a binary doubly-even self-dual [72,36,16] code exists has been open since 1973. This document does not settle the question; it maps, with certified exactness, the point at which the known methods stop, so that a future argument can begin there. On the way it proves the results…
-
Journal article
Whose Goals? Autotelic Agents Generate Goals Within Spaces They Are Given: The Goal Space and the Referee Stay Outside the Agent, and a Foundation Model in the Loop Relocates Them Rather Than Removing Them
Autotelic agents are described as learning to represent, generate, select and solve their own goals, and a new generation of systems built on foundation models is reported to do so without hand-coded goal representations, without human intervention, or from zero data. This paper…
-
Journal article
Pooling Experience Spends Independence Twice: Shared Memory in LLM Agent Teams Correlates Errors and Opens a Common-Mode Channel, Why Little of the First Was Left to Spend, and the Order, Gate and Readout That May Decide Whether Pooling Pays
Teams of language-model agents increasingly read and write a common memory: a message pool, a blackboard, a bank of distilled experience, a knowledge base served to several frameworks at once. The case for pooling is efficiency, since no agent rediscovers what another already found. The…
-
Journal article
Errors That Should Compound, and When They Do: The Product Rule Assumes Independent Steps, Conformal Guarantees Assume Exchangeable Instances, and the Unit of Independence Decides What Each Can Claim
Two bodies of work quantify uncertainty in multi-step language-model reasoning, and both rest on an independence assumption placed at some unit. Step-level error models multiply per-step reliabilities, which, at an illustrative 95% per-step accuracy, predicts that a fifty-step chain…
-
Journal article
Fingerprints That Survive What? Model Lineage Names Three Relations, Robustness Is Indexed by Which Party Is Adversarial, and a Benchmark's Distillation Column Scores the Parent That Was Not Distilled From
Language-model fingerprinting is asked to answer questions of the form "is this model derived from that one?", and its methods are routinely reported as robust to fine-tuning, quantization, pruning, merging and distillation. This paper argues that such reports are not comparable, for…
-
Journal article
Problem Choice Without a Referee: An Automated Novelty Check Certifies an Empty Search, a Rediscovery Benchmark Credits the Hypothesis Humans Found Next, and Every Located Referee of Worth Independent of Proposer and Field Needs the Objective Given
Pipelines that claim to automate scientific discovery gate their search on a judgement that a proposed idea or problem is novel and worth pursuing. The best-controlled evidence on that judgement points two ways at once: in a blind study involving more than one hundred NLP researchers…
-
Journal article
Perspectives Without Independence: Multi-Agent and Multi-Persona Reasoning Under Compute-Normalised Comparison, Why the Gains That Survive Are Not the Ones Diversity Predicts, and the Controls That Would Tell Them Apart
Multi-agent debate, multi-persona prompting and related schemes are usually justified by diversity of viewpoint: several perspectives err differently, so their combination is more reliable than any one of them. That justification is a claim about independent errors, and a scheme that…
-
Journal article
A Second Model Is Not a Second Opinion: Peer Verification, Correlated Failure, and the Independence Variable Nobody Reports
When a language model cannot reliably detect its own errors, the standard remedy is to have a second model check the first. That substitution is now the default across agent benchmarks, reward pipelines and safety evaluations, and it rests on an assumption that is rarely stated and that…
-
Journal article
The Crossover Does Not Carry the Claim: Pass@k Inversion, the Reasoning Boundary, and Five Premises the RLVR Debate Leaves Unstated
A model trained with reinforcement learning from verifiable rewards beats its base model at one sample and loses to it at many. The field has read that crossover as a measurement of the reasoning boundary and has spent two years arguing about which direction the boundary moved. This…
-
Journal article
A Step Score Is Not a Step Verdict: Three Quantities Under the Name Process Supervision, and the Outcome-Only Results That Make the Distinction Load-Bearing
Process reward models are introduced, almost without exception, as models that score whether an individual reasoning step is correct. This paper argues that the phrase "process supervision" is currently attached to three different target quantities, that the dominant scalable label…
-
Journal article
Three Rounds on Emergent Analogy: What the Webb, Hodel-West and Lewis-Mitchell Exchange Settled, How Its Third Round Tested a System While Still Claiming the Model, and Whether 'Counting' Names the Step That Analogy Theory Calls Inference
In 2023 a large language model was reported to solve text-based analogy problems zero-shot at or above the level of college students. Two critiques followed. They showed that performance on letter-string analogies collapses when the alphabet is permuted or replaced by symbols, while…
-
Journal article
Drift or New Class? Without Labels a Drifted Class and a New One Can Produce the Same Stream, the Two Lines of Work With the Most Explicit Assumptions Each Get an Answer by Freezing the Variable the Other Lets Move, and No Located Benchmark Scores the Attribution
A classifier deployed on a stream eventually sees inputs its model does not explain. Two different events can produce them: a known class can have drifted, or a class that did not exist in training can have appeared. The stream-mining literature treats these as separate problems, concept…
-
Journal article
A Trust Score Needs a Consumer: Four Places a Per-Source Trust Value Can Act on an LLM Agent, the Missing Test of a Graded Discount Against an Adaptive Attacker, and Why the Defences That Report Guarantees Use a Gate Instead of a Score
Work on trust between language-model agents produces two kinds of object. Protocol work produces identity, attestation, stake and constraint, all bound at the transport layer before any content reaches a model. Behavioural work produces a per-source score: a reputation, a credibility, a…
-
Journal article
The Reassessment That Did Not Travel: What the 2020 Arcade Result About Exploration Bonuses Establishes, the Six Conditions That Made It Informative, and Which of Them the Language-Model Revival Drops
Exploration bonuses have returned. Between 2025 and 2026 at least six frameworks added an intrinsic novelty or uncertainty term to reinforcement learning with verifiable rewards for language models, each naming a classical antecedent - prediction error, pseudo-counts, epistemic…
-
Journal article
Transfer Is a Directed Relation: Four Quantities Under One Word in the Cross-Domain RLVR Debate, and Why the Structural-Similarity Reading Does Not Survive Its Own Evidence
Reinforcement learning with verifiable rewards is the standard route to reasoning-tuned language models, and the field has split over whether its gains leave the training domain. One body of work reports that most models succeeding at mathematics fail to transfer and that single-domain…
-
Journal article
Delete Names Five Operations: Erasure Semantics for Derived Agent Memory, and Why a State-Level Definition Cannot Be the Auditable One
Persistent memory has become a standard component of language-model agents, and with it a standard assumption: that where a fact lives in a retrievable store rather than in weights, erasing it is a solved problem, because a store supports deletion and weights do not. Agent memory does…
-
Journal article
When Deliberation Hurts: Inverse Test-Time Scaling, Unfaithful Traces, and the Case Against a Unified System-2 in LLM Reasoning
The dominant frame for large reasoning models borrows a label from dual-process psychology: a fast, intuitive System 1 and a slower, deliberate System 2, with longer chains of thought read as more of the latter and therefore, on average, more reliable. A survey of reasoning LLMs states…
-
Journal article
Attribution Is Scored on a Finished Trace: What Failure-Attribution Benchmarks Measure, What Cascade Containment Would Need, and Why Neither Result Bounds the Other
A family of recent proposals aims to stop errors from spreading through LLM-based multi-agent systems: genealogy-graph governance over message dependencies, cross-channel causal monitoring, propagation-aware remediation of contaminated state, infection-aware safeguarding, and corrector…
-
Journal article
Self-Model or Self-Simulation? A Machine Self-Awareness Index Averages Sub-Scores With No Common Referent and No Fixed Sign, Why Persistent Identity, Goal Stability and Memory Continuity Are Not Evidence of Self-Access, and the Validity Tests Any Composite Would Have to Pass
Some proposals to quantify machine self-awareness combine several sub-scores - persistent identity, goal stability, cross-session memory continuity, contradiction detection, uncertainty awareness, self-prediction and introspective access - into one index. This paper asks whether such an…