4arXiv cs.CL (Computation and Language)·
DKCD: Domain knowledge-enhanced causal discovery from unstructured data using LLMs
Researchers introduce DKCD, a framework that augments LLM-based causal discovery with domain-specific knowledge retrieval and guided reasoning to address two failure modes: insufficient identification of latent causal factors and unreliable factor annotation. The system chains knowledge mining, knowledge-guided causal reasoning, and causal structure discovery into a pipeline that produces causal graphs from unstructured text in high-expertise domains like healthcare and finance. Experiments on two domain-specific datasets show improvements over baselines on both factor identification and graph construction quality.
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Study compares human and LLM active causal reasoning, finding LLMs less efficient but near human-level on conjunctive rules
A new arXiv paper investigates whether active exploration reduces the 'conjunctive handicap' in causal learning, using a blicket detector task with adult participants who could freely intervene to identify causal objects. Results show active exploration substantially improves human conjunctive causal reasoning, though conjunctive rules still require more tests than disjunctive ones. State-of-the-art LLMs approach human-level hypothesis inference accuracy but show less efficient exploration strategies and similar conjunctive-disjunctive performance gaps, raising questions about the nature of LLM causal reasoning.
KATE framework improves LLM tool calling via experiential knowledge integration and parallel reasoning
Researchers present KATE (Knowledge-Augmented Tool Execution), a framework addressing LLM failures in multi-step tool use by systematically studying knowledge acquisition, activation, and internalization. Key findings include that instance-level experiential knowledge outperforms abstract intent-level knowledge, that expanding reasoning width via parallel sampling with aggregation beats deeper chain-of-thought, and that reinforcement learning outperforms supervised fine-tuning for knowledge internalization. KATE is evaluated on BFCL-V3 and AppWorld benchmarks, showing consistent improvements over strong baselines across model scales.
CausaLab: Scalable Benchmark for Interactive Causal Discovery by LLM Agents
CausaLab is a new evaluation environment that tests LLM agents on interactive causal discovery tasks, requiring them to recover both causal graphs and structural equations from synthetic laboratory episodes governed by randomly sampled structural causal models (SCMs). The benchmark separates predictive accuracy from genuine causal understanding, revealing a persistent gap: GPT-5.2-high achieves 92% task accuracy in a 6-node observational setting but only 0.471 all-edge F1 for mechanism recovery. Mixed observation-intervention strategies improve structural fidelity, while pure intervention strategies underperform on both metrics. Premature stopping is identified as a key agent weakness, partially mitigated by prompting models to verify hypothesis-data consistency.
KDoS framework proposes distribution-optimized synthetic data for LLM knowledge injection
Researchers introduce KDoS (Knowledge Distribution-optimized Synthesis), a framework that uses a three-stage feedback mechanism guided by 'knowledge density' to optimize the distribution of synthetic training data for LLMs. Rather than stopping at preset token counts or fixed ratios, KDoS dynamically adjusts synthesis to avoid sparse or redundant domain coverage. Experiments across Qwen, Ling, and LLaMA models (0.6B–16B parameters) on 1B–5B token scales show consistent improvements over baselines on six knowledge benchmarks. A key finding is that an optimal knowledge distribution exists and remains stable across model families and scales.
Contrastive-Difference CKA reveals concept-specific structural alignment across LLM architectures
Researchers introduce CKA_Delta (contrastive-difference CKA), a training-free diagnostic that isolates concept-specific representational convergence from generic similarity across LLM architectures. The method reveals a geometric-functional universality dissociation: moderate geometric alignment coexists with near-perfect functional transfer across six concept domains and multiple architectural families. CKA_Delta also functions as an architectural outlier detector, flagging Gemma as a notable outlier (d=1.08, AUC=0.79). The work provides a practical tool for cross-architecture concept monitoring without requiring model training.
Paper argues LLMs learn causal structure via difference-making logic, not Pearl/Rubin frameworks
A new arXiv preprint proposes that LLMs learn causal structure through 'variational induction' — a difference-making logic — rather than through the dominant formalisms of Judea Pearl's interventionist approach or the Neyman-Rubin potential outcomes framework. The author analyzes how this logic is realized during training and maps specific architectural features (token embeddings, self-attention) to their roles in this inductive process. The argument draws a parallel between LLM causal learning and the experimental method of systematically varying circumstances. This is a theoretical contribution to understanding how LLMs represent causal and world-model structure.
Dep-LLM: Training-free depression diagnosis framework using structured multi-factor LLM reasoning
Dep-LLM is a training-free framework for automatic depression detection from clinical interviews that uses frozen foundation LLMs without fine-tuning. The system decomposes long clinical dialogues into five thematic factors via Chain-of-Thought analysis, applies token-level entropy-based confidence modulation, and integrates multi-factor signals for final diagnosis. Evaluated on DAIC-WOZ and E-DAIC datasets, it outperforms zero-shot baselines across 21 foundation LLMs and surpasses supervised domain-specific and commercial LLMs on multiple metrics.
BODHI: Contrastive embedding training for causal discovery in Large Behavioural Models
Researchers identify a critical failure mode in biomedical language model embeddings: off-the-shelf encoders (BioBERT, PubMedBERT, BioM-ELECTRA) assign high cosine similarity (0.76–0.92) to causally unrelated cross-domain pairs, achieving 0% accuracy on cross-domain discrimination. The paper introduces BODHI, a contrastive training approach using hard negatives mined from a biomedical knowledge graph, which improves within-vs-across-domain separation from 1.05x to 2.30x and raises discrimination gap by +0.392. The work targets Large Behavioural Models (LBMs)—foundation models that reason over personal life graphs—where false embedding proximity directly produces false causal edges. Additional contributions include an OpenVINO inference optimization achieving 133x latency reduction (1367ms to 10ms) on Intel AMX hardware, plus a counterintuitive finding that FP16 outperforms INT8 on this silicon.