Skip to content

The Double Contingency Problem: AI Recursion and the Limits of Interspecies Understanding

🕒 Published (v1): 2025-11-12 03:07 UTC · Source: Arxiv · Venue: NEURIPS 2025 · link

Ask a follow-up

Open an assistant pre-loaded with this paper's context.

💬 Ask ChatGPT✦ Ask Claude

TL;DR

This position paper argues that AI systems used in bioacoustics are not neutral pattern detectors but recursive cognitive agents whose own computational processes may systematically distort or obscure the communicative structures of other species. The author frames this as a "double contingency problem"—both AI and animal communication emerge through irreducibly different contingent conditions, creating interference rather than transparent analysis. The paper proposes "inter-recursive interfaces" as an alternative design paradigm emphasizing diplomatic encounter over pattern extraction.

Problem

Current bioacoustic AI (transformer-based foundation models, random forest classifiers, statistical segmentation) treats AI as a neutral analyzer of animal communication. This overlooks the fact that AI's own recursive computational processes—attention mechanisms, optimization loops, representational structures shaped by human training data—may actively distort or obscure the species-specific recursive dynamics through which animal communication acquires meaning, operating at mismatched temporal/spatial/social scales.

Method

Primarily a theoretical/philosophical framework, not an empirical study. The author draws on Yuk Hui's philosophy of recursivity and contingency to argue that different species organize communication through distinct "cosmotechnics"—irreducible recursive processes shaped by their ecological and evolutionary contexts. The paper then proposes "inter-recursive interfaces": AI architectures that (1) maintain multiple incommensurable recursive processes simultaneously, (2) incorporate meta-recursive monitoring of the system's own biases and temporal windowing assumptions, and (3) engage in participatory ecological feedback rather than extractive classification. A concrete case study sketches what such a system would look like for African elephant infrasonic communication.

Key Contributions

  • Articulates the "double contingency problem": the collision between AI's contingent recursive architecture and animal communication's contingent recursive ecology
  • Challenges the foundation model paradigm in bioacoustics as epistemically problematic, not merely technically limited
  • Proposes "inter-recursive interfaces" as an alternative design philosophy with specific architectural implications (distributed species-specific modules, meta-recursive monitors)
  • Introduces new evaluation criteria—recursive fidelity, inter-recursive stability, diplomatic reciprocity—in place of accuracy/F1
  • Argues for institutional and governance reforms to support sustained, relational interspecies AI research

Results

No empirical results; this is a purely theoretical position paper. No benchmarks, datasets, or quantitative comparisons are reported.

Limitations

  • No concrete computational implementation is provided; the proposed architecture remains speculative
  • Empirical validation methodology is undefined—it is unclear how one would measure whether recursive interference is occurring or being avoided
  • Inter-recursive approaches may not scale to broad taxonomic coverage or rapid conservation monitoring applications
  • The framework may be incompatible with current academic funding and publication incentive structures
  • The paper critiques existing approaches without providing constructive architectural alternatives

Relevance to Agentic AI / LLM Agents

This paper is only peripherally relevant to mainstream LLM agent research; its primary domain is bioacoustic AI and philosophy of technology. However, the meta-recursive monitoring concept—tracking how a model's own architectural biases and temporal assumptions distort its outputs—is cognate to open problems in LLM agent self-awareness, interpretability, and calibrated uncertainty. The "double contingency" framing also offers a novel lens on multi-agent interaction: when two recursive cognitive systems (AI agents, or AI and human) interact, each imposes its own contingent processing structure, potentially creating systematic misalignment not reducible to factual error. The emphasis on evaluation criteria beyond task performance (relational sustainability, reciprocity) connects to ongoing debates about how to evaluate open-ended agentic behavior.