Fodor and Pylyshyn's systematicity challenge to neural networks remains unmet, paper argues
A new arXiv preprint argues that recent claims that neural networks have met Fodor and Pylyshyn's systematicity challenge are premature. The authors specifically target Lake and Baroni's meta-learning for compositionality (MLC) protocol, showing it struggles with out-of-distribution rules and behaves unsystematically on many within-distribution problems. The paper concludes that the classical cognitive science challenge — that neural networks cannot explain systematic biconditional dependencies in language and thought — remains unresolved.
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Revisiting LLM systematicity in negation understanding via in-context learning
A new arXiv preprint analyzes how well large language models handle negation from two angles: behavioral systematicity (whether models correctly recognize negation expressions and scope) and representational systematicity (whether function vectors can be reliably constructed from in-context examples). Results show LLMs partially succeed at negation cue recognition via in-context learning but struggle with scope recognition, with performance varying by output format. Function vectors can be composed for cue extraction but are harder to extract for scope recognition tasks.
Fact checking Moravec's Paradox
A commentary piece from normaltech.ai argues that Moravec's paradox — the observation that tasks easy for humans are hard for AI and vice versa — is neither empirically accurate nor conceptually useful. The piece appears to challenge a foundational heuristic that has shaped AI capability expectations for decades. Given recent advances in robotics, vision, and language models, the argument likely draws on contemporary evidence to reframe how practitioners should think about AI difficulty gradients.
Multimodal neurons in artificial neural networks
OpenAI researchers discovered neurons in CLIP that respond to the same concept across literal, symbolic, and conceptual representations. This finding parallels multimodal neurons previously observed in biological brains and helps explain CLIP's ability to classify unusual visual renditions of concepts. The work is presented as a step toward understanding the associations and biases learned by CLIP and similar vision-language models.
Bounding Compositional Incoherence in Multi-Component LLM Agents
This paper formalizes a failure mode in multi-component LLM agent systems where individual components are locally probabilistically coherent but their composition violates basic probability axioms. The authors introduce the 'compositional residual' (eps*) as a runtime-computable measure of this incoherence, finding it positive in 33–94% of ensemble cliques across 1,876 tested configurations on a four-LLM panel. A hierarchical Boyle-Dykstra projection is proposed as a deterministic repair, and an anytime-valid e-process enables sequential monitoring. Notably, three intuitive LLM-side mitigations—retrieval, partition-aware prompting, and aggregator-LLM—each fail or regress.
Semantic vs. Surface Noise in LLM Agents: 68-Cell Measurement Study with Held-Out Validation
This paper documents an empirical phenomenon across 10 LLMs from 7 architecture families: meaning-bearing perturbations (paraphrase, synonym substitution) cause final-answer inconsistency ~19.69 percentage points more often than presentation-level perturbations (formatting, reordering) of comparable severity, across GSM8K, MATH, and HotpotQA benchmarks. The effect is validated on a held-out 11th model (qwen2.5-14B-Instruct) with 1,800 trajectories. Trace-level analysis supports a 'stealth-divergence' picture where semantic perturbations preserve the first action but induce divergence in intermediate reasoning steps, while two prior mechanism claims are explicitly retracted. The study is notable for its honest reporting of stress-test failures and pre-registered replication.
Study finds shared pattern-matching mechanisms underlie both human and LLM everyday reasoning errors
A new arXiv paper evaluates human participants and 25 LLMs on commonsense causal reasoning tasks, finding similar error patterns in both groups. The authors identify specific attention heads driving LLM responses that implement pattern-matching, and show these heads can predict human reasoning errors caused by superficially irrelevant prompt details. The findings challenge the common assumption that human reasoning relies on principled abstract world models while LLMs merely pattern-match, suggesting both may share a more unified cognitive mechanism.
NF-CoT: Latent reasoning with normalizing flows preserves autoregressive LLM advantages
Researchers propose NF-CoT, a latent reasoning framework that replaces discrete chain-of-thought token streams with continuous intermediate states modeled by normalizing flows embedded inside an LLM backbone. The approach uses a TARFlow-style normalizing flow head alongside the standard language model head, enabling exact likelihoods, KV-cache-compatible left-to-right decoding, and policy-gradient optimization in latent space. On code-generation benchmarks, NF-CoT improves pass rates over both explicit CoT and prior latent-reasoning baselines while reducing intermediate reasoning cost. The work addresses a key limitation of existing latent reasoning methods, which typically sacrifice probabilistic tractability or autoregressive compatibility.
Emergent language in multi-agent RL proposed as generative methodology for studying AI consciousness
A new arXiv preprint proposes using emergent language (EL) in multi-agent reinforcement learning as a generative methodology for studying consciousness-relevant structure in AI systems, contrasting with existing discriminative or architectural approaches. Agents begin with minimal language exposure and develop communication under task pressure alone, aiming to avoid artifacts from human language priors. As a proof of concept, the authors show agents develop self-referential communication including an echo-mismatch detection circuit that emerges from environmental affordances rather than task structure or architecture.