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ARXIV:2605.02323 · PERCEPTION · SUBMITTED 05 MAY · 20:27 UTC · FRESHNESS STALE
ARXIV:2605.02323PERCEPTIONSUBMITTED 05 MAY · 20:27 UTCFRESHNESS STALENiklas Houba · arXiv
A novel method called evidence depletion addresses slot collapse in attention models for compositional inference, significantly improving performance on tasks like gravitational-wave source detection.
Opportunity summary
Pain A novel method called evidence depletion addresses slot collapse in attention models for compositional inference, significantly improving performance on tasks like gravitational-wave source detection.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A novel method called evidence depletion addresses slot collapse in attention models for compositional inference, significantly improving performance on tasks like gravitational-wave source detection. Attention-based models perform well when components are approximately separable, as…
Compositional inference - the decomposition of observations into an unknown number of latent components - is central to perception and scientific data analysis. Attention-based models perform well when components are approximately separable, as in…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. As a result, attention fails to enforce non-redundant allocation under additive superposition. Code availability is flagged in the production record; the public repository link…
Perception moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
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A novel method called evidence depletion addresses slot collapse in attention models for compositional inference, significantly improving performance on tasks like gravitational-wave source detection.
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10.48550/arXiv.2605.02323A novel method called evidence depletion addresses slot collapse in attention models for compositional inference, significantly improving performance on tasks like gravitational-wave source detection.
Abstract
Compositional inference - the decomposition of observations into an unknown number of latent components - is central to perception and scientific data analysis. Attention-based models perform well when components are approximately separable, as in object-centric vision. Under additive superposition, however - where multiple components contribute to every observation - we identify a structural failure mode we term slot collapse: multiple slots converge to the same dominant component while weaker ones remain unrepresented. We trace this to a general limitation: attention is memoryless with respect to explained evidence. All slots repeatedly operate on the same input without accounting for what has already been explained, so gradients are dominated by the strongest component, inducing shared fixed points across slots. As a result, attention fails to enforce non-redundant allocation under additive superposition. We address this by introducing residual evidence modeling, instantiated via evidence depletion - a minimal modification combining multiplicative depletion with an attention bias. Controlled ablations show that parallel attention, sequential processing alone, and loss-based regularization fail to resolve collapse; evidence depletion, which adds residual state to sequential attention, consistently succeeds. Across synthetic benchmarks and real-world audio mixtures (FUSS), evidence depletion reduces slot collapse by up to an order of magnitude, generalizing beyond synthetic settings. On gravitational-wave source inference for the ESA/NASA LISA mission, under identical architectures, data, and losses, standard attention fails while evidence depletion prevents collapse and enables multi-source posterior estimation. These results show that under additive superposition, residual evidence tracking is the operative ingredient for preventing collapse and enabling compositional inference.
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PROBLEM
A novel method called evidence depletion addresses slot collapse in attention models for compositional inference, significantly improving performance on tasks like gravitational-wave source detection. Attention-based models perform well when components are approximately separabl...
METHOD
Compositional inference - the decomposition of observations into an unknown number of latent components - is central to perception and scientific data analysis. Attention-based models perform well when components are approximately separable, as in object-centric vision.
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. As a result, attention fails to enforce non-redundant allocation under additive superposition. Code availability is flagged in the production record; the public repository link still needs proof alignment.
WHY NOW
Perception moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A novel method called evidence depletion addresses slot collapse in attention models for compositional inference, significantly improving performance on tasks like gravitational-wave source detection. Attention-based models perform well when components are approximately separable, as in object-centric vision.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Compositional inference - the decomposition of observations into an unknown number of latent components - is central to perception and scientific data analysis. Attention-based models perform well when components are approximately separable, as in object-centric vision.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. As a result, attention fails to enforce non-redundant allocation under additive superposition. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Perception moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A novel method called evidence depletion addresses slot collapse in attention models for compositional inference, significantly improving performance on tasks like gravitational-wave source detection.
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