3arXiv cs.CL (Computation and Language)·
Schwartz-Geometry Decoding improves coherence in human value detection without sacrificing F1
A new arXiv preprint proposes injecting the circular Schwartz value continuum as an output-space geometry into multi-label classifiers for human value detection. The authors compare training-time geometry-aware objectives against a post-hoc energy decoder on a DeBERTa-v3-base model, finding that the decoder improves label-set coherence with the theoretical continuum without degrading Macro-F1 or Micro-F1. Training-time geometry injection yields only marginal gains, no better than a random ordering. A Qwen2.5-72B-Instruct diagnostic shows that supplying the continuum at inference shifts behavior but does not match supervised structured prediction.
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Systematic Study of Schwartz Value Detection in Political Texts: Context, Scale, and Moral Knowledge
This paper investigates when additional context, larger models, or retrieved moral knowledge improve detection of Schwartz human values in political text using the ValueEval benchmark format. Key findings show that full-document context helps supervised DeBERTa encoders (+3.8–4.8 macro-F1) but not zero-shot LLMs, while RAG with a curated moral knowledge base consistently benefits all model families under early fusion. Scaling model size does not guarantee gains, and simple early fusion outperforms more complex RAG variants. The study recommends jointly evaluating context, knowledge, and model family rather than assuming larger inputs or models universally improve value-sensitive NLP.
Calibrated LLM annotation and encoder transfer for measuring human values in social media text
A new arXiv preprint investigates how different LLMs, prompts, and instruction languages operationalize Schwartz's theory of basic human values when annotating non-English social media posts. The authors evaluate annotation quality beyond standard F1 metrics, examining structural alignment, error structure, and confidence-ambiguity relations, finding that iterative prompt calibration reduces misattributions. They also demonstrate that LLM annotations can be transferred to a smaller encoder model via soft-label training, preserving theory-grounded value interpretations and uncertainty information.
Language models linearly encode a 'value axis' tracking expected goal success, study finds
Researchers construct a 'value axis' in Qwen3-8B's activation space using synthetic in-context RL data, finding that this axis distinguishes high vs. low confidence, backtracking vs. non-backtracking rollouts, and correct vs. corrupted code. Steering along this axis causally modulates self-correction behavior and verbosity, while DPO training shifts the internal value of rewarded behaviors. Applied to real-world settings, the axis reveals that Qwen assigns low internal value to politically sensitive queries post-training and that SFT increases domain-specific confidence. The findings suggest LLMs linearly encode an estimate of expected goal success that shapes their generative behavior.
Transformer embeddings shown to intrinsically encode Russell's circumplex model of emotion geometry
A new arXiv paper investigates whether Transformer-based text and speech encoders (RoBERTa, wav2vec 2.0) recover the geometric structure of Russell's circumplex model of affect — a valence-arousal topology from psychology. Experiments on naturalistic datasets (MSP-Podcast) and LLM-generated stimuli show that multimodal fusion achieves perfect topological alignment with Russell's primary emotion ordering, and zero-shot generic text embeddings place fine-grained emotion terms near their human-mapped coordinates. The authors argue this structure is intrinsically encoded in the representations rather than being an artifact of labeling, bridging psychological theory and representation learning.
Framework for value-constrained credit assignment in fully delegated AI cooperatives
A new arXiv preprint proposes a framework for reward allocation in AI cooperatives where human principals are represented by agents contributing data and model updates under heterogeneous value constraints. The approach introduces value-conditioned gradient filtering and online marginal contribution signals within a 'traversal learning' (TL) substrate, which the authors argue preserves explicit gradient paths and enables finer attribution than FedAvg-style federated learning. The work positions itself against data valuation, federated contribution estimation, personalized federated learning, and pluralistic alignment research.
Joint Energy-Based Models Reveal a Generative-Discriminative Sweet Spot for Human-Aligned Vision
Researchers use Joint Energy-Based Models (JEMs) to isolate the effect of learning objective—independent of architecture, scale, and data—on human alignment in visual representations. By varying a single mixing coefficient between discriminative and generative training, they evaluate models across six human-alignment benchmarks and find that alignment peaks at intermediate points on the generative-discriminative continuum rather than at either extreme. The results suggest that hybrid objectives combining categorical structure from discriminative learning with input-structure sensitivity from generative learning yield the most human-like visual behavior. This challenges the framing of generative vs. discriminative as a binary choice for building human-aligned vision systems.
The Matching Principle: A Geometric Theory Unifying Robustness, Domain Adaptation, and Alignment via Nuisance Covariance
This paper proposes the 'matching principle': a unified geometric framework arguing that robustness methods (CORAL, IRM, adversarial training, augmentation, metric learning, Jacobian penalties, alignment constraints) are all estimators of the same object—the covariance of label-preserving deployment nuisance—and that regularizing the encoder Jacobian along this covariance's range is the core statistical problem. The authors prove closed-form optimality results in a linear-Gaussian model, introduce the Trajectory Deviation Index (TDI) as a label-free embedding sensitivity probe, and validate predictions across 13 pre-registered experimental blocks including Qwen2.5-7B. At 7B scale, matched style-PMH improves selective honesty while standard DPO degrades Style TDI, connecting the theory to alignment safety.
Internal Oppenheim-Lim test reveals phase/sign identity codes shared across image classifier architectures
A new arXiv preprint applies a causal intervention inspired by Oppenheim and Lim (1981) to probe whether trained image classifiers encode identity in Fourier phase rather than magnitude within their hidden layers. By transplanting phase or sign components between images at chosen layers in PRISM2D, GFNet, ViT-B/16, and ResNet-50, the authors find that predictions follow the phase/sign donor across all tested architectures, with image-specific magnitude largely dispensable. ResNet-50 requires a pre-ReLU intervention to reveal a latent sign code, exposing how rectification and readout geometry shape the basis in which the code is expressed. The findings offer a mechanistic account of the texture–shape gap between CNNs and attention-based models.