Tests of performance on specific tasks, including language modeling, are insufficient for determining consciousness status

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

• It is basically impossible to determine if a computer program generates conscious experience by merely observing its performance; a test for consciousness must take internal structure into account.claim0.845
  Paper's argument against behavioral tests for consciousness, establishing why MCH requires internal analysis
• There exists no viable behavioral test for consciousness analogous to the Turing Test for intelligence, because consciousness is a particular internal way to achieve performance, not externally visible performance itself.claim0.842
  Paper identifies as a research gap requiring internal analysis methods rather than behavioral benchmarks
• Consciousness in AI is best assessed by drawing on neuroscientific theories of consciousness.claim0.823
  Central methodological claim of the paper.
• Can we develop better behavioural tests for consciousness in AI that are difficult to game?question0.823
  Open question from Box 4.
• We hypothesize that general computational machines with sufficient resources possess the necessary and sufficient means to implement consciousness, and that successful implementation can be established via analysis or testing.hypothesis0.821
  The central hypothesis of the paper
• Verbal reports (the Turing Test) and homology to human brains are utterly inadequate criteria for assessing the status of novel, unconventional agents that offer no familiar touchstone of phylogeny or anatomy.claim0.815
  Core claim that standard criteria fail for novel agents.
• Can 'Consciousness' Be Observed from Large Language Model (LLM) Internal States? Dissecting LLM Representations Obtained from Theory of Mind Test with Integrated Information Theory and Span Representation Analysisconcept0.801
  The primary paper being extracted — applies IIT 3.0 and 4.0 to LLM representation sequences derived from ToM test data to investigate whether consciousness phenomena can be observed.
• We take our principal contributions in this report to be: 1. Showing that the assessment of consciousness in AI is scientifically tractable ... 2. Proposing a rubric for assessing consciousness in AI ... 3. Providing initial evidence that many of the indicator properties can be implemented in AI systems using current techniques ...quote0.800
  Summary of contributions.