Tag: cs.CL

[Submitted on 27 Sep 2026]

View PDF HTML (experimental)

Abstract:Under the accelerated pace of AI development, any benchmark whose difficulty is calibrated with humans or current AI systems becomes circular, or saturates soon. How can we then evaluate machines that may become more capable than the current machines and the people who write the tests? One answer to the question hinges on inverse problems, inductive inference questions for which difficulty depends on the complexity of the underlying mechanism, given the observations and the previous knowledge. Levin’s Kt, a computable variant of Kolmogorov complexity, puts a theoretical upper bound to that difficulty, balancing the simplest representation encoding for the mechanism and the logarithm of the time steps to run that mechanism. Previous tests derived from Levin’s Kt, such as the original C-test, are based on minimalistic Universal Turing Machines (UTM), capable of very limited representations and no previous knowledge. Reasoning large language models (LLM) are UTMs that capture a compendium of human culture as knowledge, using prompts as natural language programs. This paper presents the first test based on Levin’s Kt with LLMs as representational machines. Fresh test items can be produced at high difficulties, estimated in a ratio scale measured in bits, before any human or frontier model can solve them. We formalise the measure, devise a lightweight generation and certification pipeline, explore the effect of the power of the reference machine and the gap between examiner and examinee. We produce a first battery of certified items administered to twelve LLMs. The resulting agent characteristic curves decrease with difficulty, down to the uncharted high-difficulty region, the supra-intelligence window of items we can certify, yet beyond generations of frontier intelligence.

Submission history

From: Jose Hernandez-Orallo [view email]
[v1] Sun, 27 Sep 2026 20:41:46 UTC (541 KB)

[Submitted on 26 Sep 2026]

View PDF HTML (experimental)

Abstract:Post-training large language models (LLMs) on two-player zero-sum games provides a verifiable reward, and the gains have been shown to transfer to general reasoning. Existing pipelines, however, rely on a handful of hand-written games and on advantage estimators tied to them, so both the supply of games and the advantage estimate on a large corpus limit scaling. We present GameSmith, which scales both. An LLM synthesises games inside templates whose reward, observation, and hidden-information code is fixed. Each game is verified by seed-paired replay and by an independent re-implementation of its specification, then expanded into encoding, rule, and size variants, giving 26 new games in 29 families and 336 verified variants from the three SPIRAL templates. The model is distilled from the self-play of a larger teacher (Qwen3-32B) on these games and then trained by self-play reinforcement learning with CRAB, an advantage estimator that credits each decision against a shrinkage estimate of the expected return of its position and is defined on every variant. On Qwen3-4B-Base, distillation raises the 13-benchmark mean pass@1 from 45.5 to 62.5, above the three SPIRAL games at the same budget, and self-play with CRAB reaches 64.9. The ordering holds across models, and transfer grows with the number of game families.

Submission history

From: Waylon Li [view email]
[v1] Sat, 26 Sep 2026 23:07:02 UTC (251 KB)

[Submitted on 23 Jul 2026 (v1), last revised 13 Oct 2026 (this version, v3)]

View PDF HTML (experimental)

Abstract:Controlling combinations of personality-related tendencies is important for adapting large language models to diverse interaction needs. But successful single-trait steering does not establish how to construct interventions for joint targets, or whether existing trait directions adequately represent them. We introduce a target-first framework that directly extracts steering directions from joint personality targets and then examines their decomposition into individual directions. To support this framework, we construct NarrationDB, linking character self-narrations to automatically evaluated joint scores across eight Jungian-inspired functions, a correspondence not directly available in existing Big Five- or MBTI-oriented resources. Experiments on three models demonstrate questionnaire-score controllability across the eight functions and coordinated changes in a tested two-function target. Directly extracted joint directions are better approximated by their corresponding individual directions than by unrelated pairings, but retain substantial residual components. Our work provides a method and resource for investigating joint personality control, with evidence that individual steering directions capture part, but not all, of the structure of joint targets.

Submission history

From: Zai Liu [view email]
[v1] Thu, 23 Jul 2026 00:18:39 UTC (334 KB)
[v2] Fri, 24 Jul 2026 14:33:17 UTC (1 KB) (withdrawn)
[v3] Tue, 13 Oct 2026 12:02:48 UTC (874 KB)

[Submitted on 8 Oct 2026]

View PDF HTML (experimental)

Abstract:Language model agents increasingly operate over long interaction streams, requiring online adaptation to distribution shifts through continual learning. However, existing agent harnesses typically either evolve their states only through forward information flow or rely on additional replay or validation data for harness optimization. To address this, we propose ReHarness, a recurrent harness framework for agentic continual learning that complements forward read and write with backward revision under a strictly online streaming protocol. We ask what information could have been captured differently in the harness state to better inform the current instance in the stream. Specifically, after each instance, we leverage the resulting trajectory as feedback to revise the reflections retrieved for that instance, thereby propagating feedback backward through the textual harness state. Across three continual learning benchmarks and three language model backbones, ReHarness consistently outperforms strong raw-context baselines, memory- and reflection-based methods, and prompt optimization approaches. Further analysis shows that ReHarness produces source-grounded and meaningful revisions, corrects overgeneralized or outdated experience under distribution shifts, and improves both stability and plasticity during continual learning.

Submission history

From: Yuxin Xiao [view email]
[v1] Thu, 8 Oct 2026 19:44:43 UTC (422 KB)