attribution

LlamaParse now supports multi-layered bounding boxes (region, line, word) for granular document text attribution, improving auditability in invoices, research reports, and more.

LAPO proposes a leave-one-turn attribution method for self-generated process rewards in multi-turn search reasoning, enabling fine-grained credit assignment without external reward models. It achieves state-of-the-art results across seven datasets.

This paper introduces a training-free attribution method to identify sparse inter-layer dependencies in Transformer FFN neurons, showing that small subsets of preceding activations suffice to preserve neuron activations with high fidelity.

This paper introduces a class of nonlinear axiomatic attribution methods for cooperative games to overcome the limitations of the linear Shapley value, which has an excessively large null space. Experimental results demonstrate the potential effectiveness of these methods in terms of inclusion AUC metric compared to Shapley value variants.

The article discusses the challenge of attribution in AI agent monetization, where determining credit for conversions becomes complex when agents recommend products and influence users before clicks.

Introduces MultAttnAttrib, a training-free method for multimodal attribution in long document QA, along with the MultAttrEval benchmark. It outperforms prompting-based methods and matches frontier models like GPT-5.4.

This paper introduces turn-averaged sparse autoencoders (SAEs) that operate on average activations across conversational turns, enabling efficient feature discovery and attribution graphs for long contexts. It also proposes a nested architecture for joint training with per-token features.

Proposes Gradient-Based Connections (GBC), a method that models multi-agent LLM systems as computational graphs and uses gradient signals to attribute errors to specific agents, enabling better system-level optimization.

Introduces the Member vs Generated Inference (MGI) task to distinguish training members from generated outputs in generative models, and proposes Data Circuit Breaker (DCB), a three-stage method combining autoencoder and latent generator signals, which outperforms existing methods across autoregressive and diffusion models.

This paper introduces CAMS, a modular multi-document summarization framework that extracts atomic claims with token-level provenance, clusters equivalent claims, and rewrites them into summaries with fine-grained, multi-source traceability, significantly improving faithfulness and citation precision.

Proposes an anytime-valid attribution method that uses a human-labeled anchor set and a betting e-process to distinguish whether score drift in LLM evaluation pipelines comes from the system or the judge, resolving the ambiguity caused by silent judge changes.

The article argues that as AI agents autonomously perform actions in shared workspaces, clear attribution of each action to both the agent and the accountable human is necessary for oversight and trust. Without proper identity and audit trails, teams cannot safely delegate more complex tasks to agents.

This paper introduces IAPO, a reinforcement learning algorithm that improves tool-calling capabilities in multimodal small language models by aligning input attribution with a stronger teacher. Experiments on Qwen2.5-VL-3B show an average 3% improvement in visual question answering accuracy across six test sets.

Warner Music Group acquires Sureel AI, an AI attribution startup that uses 'AI DNA' technology to trace how AI models use songs, aiming to better protect artists' intellectual property.

This paper introduces FullCite, a framework for generating structured inline citations that link each claim to both its source document and specific evidence spans. Evaluated on three QA benchmarks (ASQA, BioASQ, ExpertQA), it finds that while LLMs are good at document-level attribution, they struggle with precise evidence span identification.

Introduces PRIG, a gradient attribution method that localizes prompt ambiguity in large language models by training a linear probe to distinguish clear from ambiguous prompts and attributing the probe score to token representations in the residual stream, achieving strong performance on synthetic and human-written benchmarks.

Big Tech companies including Meta, Google, and Apple are collaborating with Mozilla on a new browser standard for advertising attribution, raising concerns about privacy and competitive advantages.

Proposes Computational Reality Monitoring to detect when language models rely on pretraining memory rather than retrieved context, addressing the attribution blind spot in retrieval-augmented generation.

This paper introduces CUDAnalyst, a tool for analyzing how individual feedback signals influence planning decisions in self-evolving LLM agents for CUDA kernel generation, using trajectory freezing and selective feedback injection to enable controlled attribution.

Agent Trace is an open specification for tracking AI-generated code in version-controlled codebases, defining a vendor-neutral format to record AI contributions alongside human authorship.