The Frame Problem
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The Frame Problem
The Frame Problem To most AI researchers, the frame problem is the challenge of representing the effects of action in logic without having to represent explicitly a large number of intuitively obvious non-effects. But to many philosophers, the AI researchers' frame problem is suggestive of wider epistemological issues. Is it possible, in principle, to limit the scope of the reasoning required to derive the consequences of an action?
Formalizing Learning from Language Feedback with Provable Guarantees
Announce Type: replace Abstract: Interactively learning from observation and language feedback is an increasingly studied area driven by the emergence of large language model (LLM) agents. Despite impressive empirical demonstrations, so far a principled framing of these decision problems remains lacking. We formalize the Learning from Language Feedback (LLF) problem, assert sufficient assumptions to enable learning despite latent rewards, and introduce $\textit{transfer eluder dimension}$ as...
DuMate-DeepResearch: An Auditable Multi-Agent System with Recursive Search and Rubric-Grounded Reasoning
arXiv:2606.07299v1 Announce Type: new Abstract: Deep Research (DR) has emerged as a new agentic paradigm to tackle complex, open-ended research tasks, demanding systems that can iteratively frame problems, acquire evidence, verify sources, and synthesize long-form reports. In practice, however, current DR systems are constrained by four interrelated limitations: long-horizon planning over an underspecified scope, the bottleneck of decomposing and scheduling such tasks within a single agent,...
Beyond ReLU: Bifurcation, Oversmoothing, and Topological Priors
Announce Type: replace Abstract: Graph Neural Networks (GNNs) learn node representations through iterative network-based message-passing. While powerful, deep GNNs suffer from oversmoothing, where node features converge to a homogeneous, non-informative state. We re-frame this problem of representational collapse from a \emph{bifurcation theory} perspective, characterizing oversmoothing as convergence to a stable ``homogeneous fixed point.''
HDSL: A Hierarchical Domain-Specific Language for Structured 3D Indoor Scene Generation and Localized Editing with LLM Agents
arXiv:2606.09738v1 Announce Type: new Abstract: Text-driven indoor scene generation and editing require an intermediate representation that language models can both produce and revise. Existing LLM-based systems often rely on scene graphs or global constraint lists, which are compact but underspecify local geometry and make instruction-based edits difficult to localize. We frame this problem as structured program generation and local program repair, and propose Hierarchical Descriptive Scene...
Semantic-Structural Alignment for Generative Pictorial Charts
new Abstract: Traditional statistical graphics are precise but often lack the visual appeal, memorability, and engagement of pictorial charts. We present a generative framework for the automated synthesis of pictorial charts that bridges the gap between semantic expression and structural faithfulness. Rather than treating charts merely as images to be stylized, we frame the problem as a dual-conditioned generation task guided by two parallel external control signals: a text prompt capturing...
Supervised Learning as Lossy Compression: Characterizing Generalization and Sample Complexity via Finite Blocklength Analysis
arXiv:2602.04107v2 Announce Type: replace Abstract: This paper presents a novel information-theoretic perspective on generalization in machine learning by framing the learning problem within the context of lossy compression and applying finite blocklength analysis. In our approach, the sampling of training data formally corresponds to an encoding process, and the model construction to a decoding process. By leveraging finite blocklength analysis, we derive lower bounds on sample complexity...
Forgetting is Not Erasure: Recovering Latent Knowledge via Transport Keys
arXiv:2606.02860v1 Announce Type: new Abstract: Catastrophic forgetting is often framed as a representational problem: after sequential training, a model appears to lose the features that supported performance on earlier tasks. We challenge the stronger form of this view. Across controlled continual-learning settings, we find that a significant portion of apparent forgetting can be attributed to interface drift between internal stages rather than permanent erasure of task-relevant computation.
Morgan Riddle sparks backlash over response to dating article
Morgan Riddle, the girlfriend of American tennis star Taylor Fritz, has sparked discussion online after criticizing a New York Times opinion article that was published ahead of Pride Month. The tennis influencer shared her thoughts on Instagram, where she argued that the article carried a misogynistic message and unfairly framed women as the problem in modern dating conversations. The article was originally published under the headline “Being Straight is Great Actually” before later being...
Predictive radar tracking reveals >500 mV/m electric-field transients during the May 2024 superstorm
arXiv:2605.31046v2 Announce Type: replace Abstract: The bulk motion of E-region radar aurora provides a sparsely distributed, direct measurement of the ionospheric electric field in intermittent bursts. We present a tracking procedure for \textsc{icebear} VHF measurements of Farley-Buneman waves. Each cluster is represented as an $\alpha$-shape; frame-to-frame association is a Hungarian linear-assignment problem with a cost combining centroid distance and shape Intersection-over-Union;...