Causal Semantic Alignment
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Related Articles from SNS
Causal Semantic Alignment for LLM-based Time Series Forecasting
arXiv:2606.08262v1 Announce Type: new Abstract: Recent advances in Large Language Models (LLMs) have opened new possibilities for time series forecasting by enabling alignment between temporal patterns and pretrained word embeddings. However, most LLM-based methods overlook the heterogeneous nature of time series, where dynamic fluctuations and invariant semantics are entangled. This entanglement introduces spurious correlations during the alignment, as dynamic components act as confounders...
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...
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...
CausalPOI: Spatio-Temporal Graph-Based Causal Modeling for Cold-Start POI Check-in Forecasting
arXiv:2606.05413v1 Announce Type: new Abstract: As urban environments continue to evolve rapidly, accurately modeling the dynamic behaviour of Points of Interest is essential for supporting data-driven urban planning and commercial decision-making. While recent advancements in spatio-temporal graph learning have improved POI forecasting, most methods rely on proximity-based graphs and correlation-driven modeling, which overlook the functional dependencies between POIs and fail to capture the...
Beyond Post-hoc Explanation: Toward Glassbox AI via Probabilistic Mediation
Announce Type: new Abstract: Large language models are rapidly becoming infrastructural components in high-stakes institutional settings, including public administration, legal reasoning, and healthcare, where opacity is not merely inconvenient but institutionally and legally untenable. Existing approaches to explainability are predominantly post-hoc, offering unstable, non-contestable accounts that have no formal relationship to the reasoning process that produced the output. We argue that...
Visual Instruction Tuning Aligns Modalities through Abstraction
arXiv:2606.03871v1 Announce Type: new Abstract: Visual instruction tuning effectively adapts a pre-trained Large Language Model (LLM) to process image information alongside text. Yet, it remains unclear how visual features are embedded into the layer-wise hierarchy of abstractions of the LLM backbone. Across a diverse set of vision-language architectures, we show that instruction tuning primarily serves as a bridge, embedding visual features directly into the intermediate semantic layers of...
Magenta RealTime 2: Open and Local Live Music Models
We’re excited to share Magenta RealTime 2 (MRT2), a state-of-the-art open model and efficient real-time inference engine that enables you to build and play AI musical instruments on your laptop! To get started, download the apps on your MacBook (requires Apple Silicon). Unlike other large generative music models that work offline to turn a prompt into a track, MRT2 is a live, interactive model that you can control with MIDI and audio, in addition to text.
LLMs are not the black box you were promised
LLMs are not the "black box" you were promised. Mechanistic interpretability — peering into a neural network to reverse engineer its inner workings — has made major strides. Anthropic's On the Biology of a Large Language Model (2025) is a landmark in that effort.