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arXiv Machine Learning

Toward Machine Learning with the Unit as a Primitive: Learning from Unit-Linked Events

The paper proposes treating the ‘unit’—a persistent referent that multiple events may refer to—as an explicit primitive in machine learning tasks. It formalizes supervised learning as learning a pair of a tokenizer that generates a contextual unit token and a shared response law that uses this token, thereby distinguishing homogeneous from heterogeneous worlds. The work also introduces concepts such as unit abduction and trusted resolvers to handle cases where unit identity is unresolved.

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arXiv AI
Sep 1

Wide Learning: Learning to Reach Evidence

The paper introduces the concept of Wide Learning, which examines how a learner’s internal state can expand its ability to generate informative evidence under fixed resources and primitive affordances. By formalizing effective epistemic reach—defined by learner state, deployment budget, reliability threshold, and evaluation distribution—the authors demonstrate, through a controlled construction, that learning can significantly alter the probability of successfully realizing a diagnostic that was previously unlikely. The study shows that even with identical observable laws, a calibrated learner can achieve perfect diagnostic realization, highlighting the impact of learning on the scope of attainable evidence.

By Junzhou Chen
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arXiv AI
Oct 2

Rules Amortize, Pairings Don't: Linguistic Structure Determines What Latent Task Representations Can Replace In-Context Learning

The paper investigates which linguistic operations can be replaced by latent task representations instead of in‑context prompts. A 2.6 M‑parameter network learns to encode the geometry of a few‑shot support set and applies an additive update to a frozen GPT‑2 model, achieving zero‑shot performance comparable to or better than 10‑shot in‑context learning for forward inflection and lemmatization. However, for arbitrary pairings such as antonymy, the latent approach only reaches about half the performance of in‑context learning, indicating that productive rules amortize well while memorized pairings do not.

By Gunmay Jhingran
llms
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arXiv AI
Aug 24

World models of environment, agent and joint agent-environment systems

The paper introduces a framework that distinguishes world models by the channel they represent—environment, agent, or joint agent‑environment—using computational mechanics to define canonical predictive models as ε-transducers or ε-machines. It shows how closed‑loop coupling induces support‑restricted models whose states factor through the joint causal states, and demonstrates with a POMDP example that such restriction can reduce an otherwise infinite‑state environment model to a finite one.

By Manuel Baltieri, Filippo Torresan, Yivan Zhang, Alexander Boyd, Fernando E. Rosas
agentsreinforcement-learning
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arXiv Machine Learning
Sep 3

IDEEA: training-free Input-Dependent stEEring via Activation cluster matching

IDEA is a training‑free, input‑dependent steering method for large language models that matches activations to cluster‑specific directions aligned with a target concept. It clusters positive and negative activation supports per attention head, solves an optimal‑matching problem to create a pool of cluster‑conditional directions, and selects the best match for each input at inference time. This approach preserves the input’s original representation while improving the truth × info rate on TruthfulQA by an average of 9.9% (up to 23.5%) over input‑independent baselines.

By Zheng Wang, Muchen Li, Renjie Liao, Yan Leng
llmsreinforcement-learningfine-tuningsafety
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