arXiv AI
Aug 18
From Contexts to Values: Context-Dependent Defeat in Abstract Argumentation
arXiv:2608. 15536v1 Announce Type: new Abstract: In value-based argumentation, an audience's ordering of values decides which attacks succeed as defeats.
From Contexts to Values: Context-Dependent Defeat in Abstract Argumentation
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arXiv AI
Jul 21
Choosing the Lens: Strategic Perspective Activation in Context-Dependent Argumentation
arXiv:2605. 31581v2 Announce Type: replace Abstract: The same arguments often need to be evaluated under different external regimes.
arXiv Machine Learning
Aug 17
Identifiability and Order-Dimension Limits of In-Context Learning on Partial Orders
arXiv:2608. 14004v1 Announce Type: new Abstract: In-context learning is commonly formalized as inference from examples of a function.
arXiv Computation and Language
Sep 2
Compile, Don't Memorize: A Context Compilation Architecture (CCA) for In-Context Learning
arXiv:2609.00759v1 Announce Type: new Abstract: Large language models (LLMs) increasingly handle in-context learning (ICL) tasks where a long, novel context defines the rules, knowledge, and output s...
arXiv AI
Aug 26
Rules Before Oracles: Auditable, User-Configurable Argument Selection for Deliberative Polling
The paper proposes a transparent, user‑configurable rule for selecting arguments in deliberative polls, replacing opaque learned rankers. It formalises argument selection over bipolar justification sets, introduces seven civic recommender criteria, and presents a one‑hop reversed endorsement flow rule that meets them. Experiments on 17,000 simulated runs show the rule performs comparably to random on coverage but outperforms other methods on endorsement mass and robustness under adversarial pressure.
arXiv AI
Sep 1
SimGuide: Typed Multi-Context User Representations for Preference-Conditioned Agent Planning
arXiv:2608.24888v2 Announce Type: replace Abstract: Agents that act on a user's behalf must plan differently for different users, and increasingly do so from some structured representation of user co...
arXiv Machine Learning
Jun 9
Finite Certificates for In-Context Determinacy and a Threshold Theory of Emergence in Language Models
arXiv:2606. 07623v1 Announce Type: new Abstract: This paper develops a model-theoretic framework for verifying context-conditioned language-model behavior by replacing benchmark labels with finite semantic certificates.
Hugging Face Trending Papers
Jul 9
Understanding Axes of Difficulty For Long Context Tasks Via PredicateLongBench
Large language models (LLMs) have demonstrated rapidly improving long-context capabilities, prompting a wave of benchmarks designed to evaluate them. However, existing long-context evaluations - from Needle-in-a-Haystack (NIAH) tests to more recent multi-hop reasoning and summarization tasks - predominantly measure average-case performance, and many are either saturated or lack robustness.
arXiv AI
Jun 16
Constitutional Value Potentials: reading and steering internal priority margins in language models
arXiv:2606. 15420v1 Announce Type: cross Abstract: A constitution tells a language model what to value, but little tells us whether it does.
arXiv AI
Jun 10
From Context-Aware to Conflict-Aware: Generalizing Contrastive Decoding for Knowledge Conflict in LLMs
arXiv:2606. 10298v1 Announce Type: new Abstract: When large language models generate from retrieved or augmented contexts, conflicts between external context and parametric priors remain a central reliability bottleneck.
arXiv AI
Jul 10
Understanding Axes of Difficulty For Long Context Tasks Via PredicateLongBench
arXiv:2607. 08284v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated rapidly improving long-context capabilities, prompting a wave of benchmarks designed to evaluate them.
Hugging Face Trending Papers
Jul 23
Best-of-Evidence: Best-of-N Selection under Partial Verification
BoN improves model outputs by sampling several candidates and selecting one with a proxy score, but it assumes that complete candidates can be evaluated reliably. Many vision-language tasks instead provide only partial verification: a finding, span, value, region, or relation may be checkable even when no dependable whole-response verifier exists.