Semantic Equilibrium
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Related Articles from SNS
Equilibrium Semantics and Strong Equivalence for Higher-Order Logic Programs
arXiv:2606.02387v1 Announce Type: new Abstract: One of the most significant achievements of equilibrium logic was the characterization of strong equivalence, a property crucial for program transformation and optimization in Answer Set Programming (ASP). While ASP has recently been extended to a higher-order setting to enhance its expressive power, the lack of a comparable purely logical foundation has made verifying strong equivalence for higher-order programs or even proving the correctness...
Reducing Arbitrary Metric Temporal Formulas into Logic Programs under Answer Set Semantics
arXiv:2605.30618v1 Announce Type: new Abstract: Metric temporal equilibrium logic (\MEL) extends temporal equilibrium logic (\TEL) by incorporating quantitative timing constraints, enabling the specification and analysis of deadlines and durations. \MEL\ is particularly suited for domains where time-bound properties are crucial, such as embedded systems, cyber-physical systems, and real-time software.
Adaptive Causal Alignment for High-Confidence Adversarial Training
new Abstract: Inverse adversarial training leverages high-confidence predictions to stabilize robust learning, yet we uncover a critical paradox: high confidence often stems from overfitting to non-causal background correlations rather than intrinsic object semantics. Our investigation reveals that visual context functions as a dual-natured signal, serving as either a necessary supportive prior or a spurious confounder. This insight renders existing blind suppression strategies flawed, as...
UniAudio-Token: Empowering Semantic Speech Tokenizers with General Audio Perception
arXiv:2605.31521v1 Announce Type: new Abstract: Semantic speech tokenizers have become a widely used interface for Audio-LLMs, owing to their compact single-codebook design and strong linguistic alignment. However, their focus on linguistic abstraction induces acoustic blindness, limiting their applicability beyond speech-centric tasks. We propose UniAudio-Token, a framework that empowers semantic tokenizers with general audio perception without compromising speech ability.
STEPS: Semantic-Contract-Guided Scheduling for LLM-Assisted Natural-Language-Driven Edge AI Services
arXiv:2606.09537v1 Announce Type: new Abstract: Networked AI services are increasingly delivered through edge infrastructures to support latency-sensitive applications. Edge scheduling is critical for deciding where and how AI services are executed under limited communication and computing resources. Existing frameworks usually assume that requirements are given as numerical constraints, such as latency bounds, energy budgets, or cost limits.
Causal Mirage Equilibrium in Agentic Machine Intelligence
arXiv:2606.03636v1 Announce Type: new Abstract: Classical game-theoretic solution concepts assume that agents' internal representations remain causally linked to external states. In generative machine intelligence, this assumption fails: semantic representations can decouple from physical reality, stabilizing into self-reinforcing, operationally robust configurations. This paper introduces the risk-sensitive mean-field-type \emph{Causal Mirage Equilibrium} (CME), a solution refined concept...
S-SPPO: Semantic-Calibrated Self-Play Preference Optimization
arXiv:2606.01561v1 Announce Type: new Abstract: Aligning Large Language Models (LLMs) with human preferences is often formulated via Direct Preference Optimization (DPO). However, the standard Bradley-Terry instantiation of DPO is limited in modeling common departures from transitivity in human preferences. To address this, recent work has introduced Self-Play Preference Optimization (SPPO), which iteratively refines the policy by training on self-generated win-lose pairs.
Generative Criticality in Large Language Model Temperature Scaling
arXiv:2606.06238v1 Announce Type: new Abstract: We propose a statistical-field framework for text generated by large language models (LLMs), treating token embeddings as continuous spin variables on a one-dimensional chain. Defining a susceptibility from the connected two-point correlator and an order parameter from the ensemble-averaged embedding field, we vary the \texttt{softmax} temperature $T$ and observe a sharp susceptibility peak near a characteristic $T_c$ with power-law-like...
Universal Decision Learners
Announce Type: new Abstract: Many theories of decision making -- planning, reinforcement learning, causal intervention, online learning, and game-theoretic equilibrium -- turn local information into globally coherent behavior. This paper proposes a common categorical formulation: a Universal Decision Learner (UDL) extends a partially specified decision functor from observed contexts to new contexts by a pair of universal constructions. Left Kan extensions express rollout, aggregation, and...
Energy-Regularized Spatial Masking: A Novel Approach to Enhancing Robustness and Interpretability in Vision Models
arXiv:2604.06893v3 Announce Type: replace Abstract: Deep convolutional neural networks achieve remarkable performance by exhaustively processing dense spatial feature maps, yet this brute-force strategy introduces significant computational redundancy and encourages reliance on spurious background correlations. As a result, modern vision models remain brittle and difficult to interpret. We propose Energy-Regularized Spatial Masking (ERSM), a novel framework that reformulates feature selection...