Operational Proto-Introspection in Looped Language Models: Process-Quality Taps, Executable Branching, and the Readout-Control Boundary
- URL: http://arxiv.org/abs/2607.18553v3
- Date: Tue, 28 Jul 2026 16:28:19 GMT
- Title: Operational Proto-Introspection in Looped Language Models: Process-Quality Taps, Executable Branching, and the Readout-Control Boundary
- Abstract summary: Can a language model read the quality of its ongoing computation?<n>Can an external intervention turn that readout into better outcomes?<n>We test both questions in a frozen 2.6B looped transformer, Ouro-RLTT.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Can a language model read the quality of its ongoing computation, and can an external intervention turn that readout into better outcomes? We test both questions in a frozen 2.6B looped transformer, Ouro-RLTT. On GSM8K, a strict pre-answer probe excludes the answer region and gold value yet predicts success: hidden states plus length/log-probability features reach AUROC 0.797 versus 0.731 for those surface features alone (increment +0.066; task-clustered 95% CI [+0.021,+0.112]; 170 tasks). On Horizon Logic, a prospectively extended task-disjoint study gives an increment of +0.111 (CI [+0.056,+0.169]), independently replicated on the new cohort (+0.095) and robust to an adversarial malformed-sibling shortcut. Recurrence also moves candidate-quality readability to progressively earlier physical depth; the trend replicates across the Ouro family and qualitatively in out-of-family Huginn, although their transfer geometry differs. The readout converts into validated decision-level gains. Hidden-state-based scores improve risk-coverage over shortcut-only scores in four sealed selective-prediction arms, and terminal selection beats matched random even when every candidate is well formed (27/32 correct selections versus 64.8% expected; p = 0.0086). Generative control does not convert: directional steering is negative, a branch screen is bounded, and exact-compute loop allocation and minimal LoRA direction-binding detect no gain. These tests run through bit-exact branch/carry/prune machinery over Ouro's 192-slot recurrent cache, including a suffix-recompute splice saving up to 88% of per-branch layer passes. We call this decision-usable but not generatively controllable property operational proto-introspection. All load-bearing values use source-item-disjoint splits and antisymmetrized pairwise evaluation.
Related papers
- Why Does the Future Branch? Identifiable Closure Tests for Stochastic Physical World Models [0.0]
ClosurePairs crosses compatible microstates with repeated disturbances.<n>It recovers source attribution at unchanged likelihood.<n>It reduces equal-budget decomposition error in a nonlinear interaction benchmark.
arXiv Detail & Related papers (2026-08-01T11:07:24Z) - Beyond Accuracy: Auditing Spatial Provenance in Visual Token Pruning for OCR-Critical MLLM Inference [22.335515110971475]
Visual-token pruning is judged by answer quality at a fixed retention budget.<n>We turn this blind spot into an evidence-risk audit that couples answer behavior with geometric token-origin provenance.<n>We show that favorable target-verification points do not imply task-general compression.
arXiv Detail & Related papers (2026-07-29T13:45:31Z) - Level, Sharpness, and Corpus: Why Zero-Shot OOD Detector Rankings Do Not Transfer [36.02174685737611]
We find that detector rankings reverse across deployments, every detector exceeds $80%$ FPR95 on at least one domain, and the preferred detector depends on both the in-distribution data and the underlying VLM.<n>We introduce the Complementary Evidence Guard (CEG), a detector-agnostic wrapper that preserves complementary evidence through a non-compensatory fusion of the base detector, level, and sharpness.
arXiv Detail & Related papers (2026-07-29T08:00:49Z) - Fantastic Adaptive Taxonomies and How to Use Them [100.20614173157708]
AdaMAST converts execution traces into a compact, evidence-grounded failure taxonomy.<n>No code is hand-authored, no trace human-annotated.<n>In agent-system search, taxonomy-coded diagnoses of failed candidates outperform free-form.
arXiv Detail & Related papers (2026-07-17T17:41:16Z) - Know Before You Fetch: Calibrated Retrieval-Budget Allocation for Retrieval-Augmented Generation [10.60293484295824]
We formulate adaptive RAG as calibrated retrieval-budget allocation.<n>We calibrate sequence log-probability and prefix-logit uncertainty signals into probabilities of correctness.<n>For sequence log-probability, ECE drops from 0.275 to 0.062 on TriviaQA, 0.643 to 0.009 on NQ, and 0.711 to 0.031 on MS MARCO.
arXiv Detail & Related papers (2026-06-29T08:36:33Z) - Beyond Logprobs: A Multi-Signal Confidence Engine for LLM-Based Document Field Extraction [2.1997815944423516]
ExtractConf is a cross-domain, field-agnostic confidence engine.<n>It grounds confidence estimation in two structurally different readings of the same document.<n>It achieves 0.928 ROC AUC and reduces selective prediction risk by 70% over logprob-mean.
arXiv Detail & Related papers (2026-06-23T10:58:08Z) - When Agents Commit Too Soon: Diagnosing Premature Commitment in LLM Agents [0.0]
Long-answer LLM agents can fail quietly: they settle on one reading of the evidence early, then spend the rest of the run defending it.<n>We define representational commitment as cross-run hidden-state convergence at a fixed reasoning step.<n>A runtime monitor detects inconsistent trajectories from hidden states at AUROC up to 0.97 (0.85--0.88 under a stricter split)
arXiv Detail & Related papers (2026-06-22T07:13:13Z) - CRANE: Knowledge Editing for Reasoning MLLMs [37.084419383244814]
multimodal large language models (MLLMs) generate explicit chain-of-thought (CoT) reasoning before producing answers.<n>On reasoning MLLMs, methods that generalize (FT, LoRA) trigger format collapse, while methods without deep modification cannot generalize.<n>We propose CRANE, a retrieval-augmented framework that requires no per-edit parameter modification.
arXiv Detail & Related papers (2026-06-08T05:01:11Z) - Automated Proving of Shannon-Type Entropy Inequalities via Fine-Tuned Language Models and Guided Tree Search [50.16356451328644]
Proving Shannon-type entropy inequalities is a fundamental task in information theory.<n>We investigate whether small-scale large language models, fine-tuned on atomic proof steps, can automate this process.<n>We show that GPT-5.5 solves 1.7% samples under zero-shot prompting while Psitip solves 33.3% samples.
arXiv Detail & Related papers (2026-06-04T05:43:12Z) - Scaling Laws for Agent Harnesses via Effective Feedback Compute [53.68149869349268]
emphEffective Feedback Compute (EFC) is a trace-level scaling coordinate that credits feedback only when it is informative, valid, non-redundant, and retained for subsequent decisions.<n>EFC-based coordinates consistently predict failure rates better than raw-compute baselines.
arXiv Detail & Related papers (2026-05-28T09:45:47Z) - Reading Task Failure Off the Activations: A Sparse-Feature Audit of GPT-2 Small on Indirect Object Identification [0.0]
We report a small, reproducible audit of sparse-autoencoder (SAE) features of GPT-2 small fire differently on failed versus successful trials.<n>On 300 prompts, GPT-2 small reaches 79.7% accuracy; 146 of the 24,576 features in the layer-8 residual-stream SAE release of Bloom (2024) clear a Holm-corrected significance threshold.<n>The strongest single correlate of failure -- feature 17,491, d=+2.93, Neuronpedia label 'cryptographic keys' -- is essentially silent except when the prompt's transferred object is 'the keys'
arXiv Detail & Related papers (2026-05-21T16:55:27Z) - Amplifying, Not Learning: Fine-Tuned AI Text Detectors Amplify a Pretrained Direction [51.56484100374058]
Text detectors amplify a pretrained typicality axis; they do not construct an AI-vs-human boundary.<n>On raw encoders before any task supervision, projecting onto centroid(AI)-centroid(HC3) achieves NYT-vs-HC3 AUROC 0.806/0.944/0.834 across three architectures.<n>On RoBERTa-base, raw projection exceeds fine-tuning; on RoBERTa-base, full fine-tuning reduces discrimination below raw on both fluent-formal populations tested.
arXiv Detail & Related papers (2026-05-20T19:08:38Z) - Reading Calibrated Uncertainty from Language Model Trajectories [46.663987199083245]
Methods that probe the model's internal activations feed raw hidden states into opaque snapshots and leave implicit the layer-wise trajectory by which a representation is formed.<n>We extract eleven scale-invariant geometric features, tracing the cumulative path of per-layer updates, and feed them to a sparse linear probe.<n>The probe outperforms MSP under selective abstention, with baseline scaling with up to 21 AURC points.
arXiv Detail & Related papers (2026-05-19T19:24:29Z) - Distributional Energy-Based Models for Uncertainty-Aware Structured LLM Reasoning [40.342912574072024]
Large Language Models produce structured outputs such as travel plans or code solutions.<n>Individual reasoning steps may appear correct while the output as a whole violates budgets, fails test cases, or contradicts earlier deductions.<n>We propose a learned quality scorer with deterministic analytical constraint penalties for verifying structured LLM outputs.
arXiv Detail & Related papers (2026-05-15T17:08:27Z) - LaTER: Efficient Test-Time Reasoning via Latent Exploration and Explicit Verification [35.08680804423239]
Chain-of-thought (CoT) reasoning improves large language models (LLMs) on difficult tasks, but it also makes inference expensive.<n>We propose Latent-Then-Explicit Reasoning (LaTER), a two-stage paradigm that first performs bounded exploration in a continuous latent space.<n>LaTER projects final-layer hidden states back to the input embedding space, preserves the latent KV cache, and uses entropy and model-native stop-token probes to decide when to switch.
arXiv Detail & Related papers (2026-05-08T06:23:58Z) - Relational Preference Encoding in Looped Transformer Internal States [0.0]
We investigate how looped transformers encode human preference in their internal iteration states.<n>Using Ouro-2.6B-Thinking, a 2.6B- parameter looped transformer with iterative refinement, we extract hidden states from each loop iteration.<n>We train lightweight evaluator heads to predict human preference on the Anthropic HH-RLHF dataset.<n>Our pairwise evaluator achieves 95.2% test accuracy on 8,552 unseen examples, surpassing a full-batch L-BFGS probe (84.5%) while the base model remains completely frozen.
arXiv Detail & Related papers (2026-04-10T20:00:49Z) - TARG: Training-Free Adaptive Retrieval Gating for Efficient RAG [46.122203287541005]
Training-free Adaptive Retrieval Gating (TARG) is a single-shot policy that decides when to retrieve using only a short, no-latency draft from the base model.<n>On NQ-Open, TriviaQA, and PopQA, TARG consistently shifts the accuracy-efficiency frontier.
arXiv Detail & Related papers (2025-11-12T23:09:52Z) - Lazy Layers to Make Fine-Tuned Diffusion Models More Traceable [70.77600345240867]
A novel arbitrary-in-arbitrary-out (AIAO) strategy makes watermarks resilient to fine-tuning-based removal.
Unlike the existing methods of designing a backdoor for the input/output space of diffusion models, in our method, we propose to embed the backdoor into the feature space of sampled subpaths.
Our empirical studies on the MS-COCO, AFHQ, LSUN, CUB-200, and DreamBooth datasets confirm the robustness of AIAO.
arXiv Detail & Related papers (2024-05-01T12:03:39Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.