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.
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.
Related stories
Hugging Face Trending Papers
Aug 16
From Contexts to Values: Context-Dependent Defeat in Abstract Argumentation
In value-based argumentation, an audience's ordering of values decides which attacks succeed as defeats. In many settings the deciding factor is not the audience but the circumstances: the same attack...
arXiv AI
Jun 2
Rashomon Memory: Towards Argumentation-Driven Retrieval for Multi-Perspective Agent Memory
arXiv:2604. 03588v3 Announce Type: replace Abstract: AI agents operating over extended time horizons accumulate experiences that serve multiple concurrent goals, and must often maintain conflicting interpretations of the same events.
arXiv AI
Oct 5
How to Have a Sensitive Debate: An Instance-Optimal Protocol for AI Debate
The paper introduces a new AI debate protocol that improves upon previous designs for problems with stable decompositions into subproblems. It guarantees correctness in a worst‑case sense, establishes honesty and correctness as a dominant‑strategy equilibrium for both debaters, and proves instance‑wise optimality through black‑box lower bounds. The authors connect stable problem decompositions to fractional block sensitivity from query complexity to achieve these results.
arXiv AI
Sep 7
Measuring AI Accountability Through Argumentation Analysis: Can Model Reasoning Withstand Scrutiny?
The paper proposes a new method for evaluating AI accountability by analyzing the structural quality of a model’s defense for its decisions, using a four‑phase dialectical protocol based on Walton’s argumentation schemes and Govier’s criteria. Applied to nine large language models and 200 ambiguous moral-choice items, the study finds that models generally defend their reasoning well above the rubric minimum, though failures cluster on grounds and sufficiency and correlate with epistemic hedging. The protocol also reveals that models often present different argument schemes in justification than in reasoning, detects indefensible defenses, and highlights challenges in assessing retraction in AI alignment.
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 Machine Learning
Aug 4
OTAP: Structure-Aware Optimal Transport for Evaluating Planning and Execution in Agent Trajectories
arXiv:2607. 17082v2 Announce Type: replace-cross Abstract: Large language model agents solve tasks by generating trajectories that interleave planning, tool calls, and intermediate results.
arXiv AI
Sep 24
Alignment of LRMs via Counter-Aligned Few-Shot Conversation Exposure
Large Reasoning Models (LRMs) use explicit chain‑of‑thought reasoning and large context windows to perform complex tasks, but these features create new attack surfaces. The paper introduces SRCF, an attack that steers LRMs by prepending counter‑aligned few‑shot conversations with explicit CoT traces, causing unsafe outputs on harmful queries and unwarranted refusals on benign ones, without needing model internals. To counter this, the authors propose ARCF, a post‑training defense that exposes models to counter‑aligned conversational contexts while enforcing aligned targets, improving safety and helpfulness without harming utility.
arXiv AI
Jul 24
Robust Critics: Defending LLMs Against Multi-Turn Attacks
arXiv:2607. 20472v1 Announce Type: new Abstract: When a user asks a language model something harmful, is it a genuine attack or a misunderstood but well-meaning question?
arXiv AI
Aug 20
A Theory of Post-hoc Debate Judgement
The paper proposes a theory for judging post-hoc debates in AI, focusing on properties like reproducibility, robustness, groundedness, and explainability. It evaluates two debate‑judgement methods—LLM judges and formal computational argumentation semantics—finding similar accuracy but noting that argumentation semantics offers stronger formal guarantees. The study suggests that argumentation semantics is a preferable framework for principled debate judges in AI systems.
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 Computation and Language
Sep 16
Benchmarking Factual Robustness of LLMs via Multi-conversation Persuasion
The paper introduces the SAST-IR framework to evaluate large language models’ robustness against persuasion attacks in a memory‑less setting, revealing a flaw called "Refusal Inertia" that masks true vulnerability. Using the CP‑Agent and a custom CounterFact‑Strict dataset, the authors demonstrate that simple, diverse attack strategies achieve a 96% success rate, while complex attacks often trigger defensive compliance. The study highlights severe brittleness in current state‑of‑the‑art models when deprived of conversation history.