△ Mechanism confirmed, baseline not beaten
2026
Replace the exact matrix-polar normalization in Muon with the smoothed feedback \(h_\epsilon(M)=M(M^\top M+\epsilon I)^{-1/2}\). This retains singular-vector-aware updates and approximately unit-normalizes dominant spectral modes, but avoids unstable behavior when the momentum matrix is rank deficient or has tiny singular values.
Useful7/10
Difficulty5/10
Novelty4/10
✗ Failed on benchmark
2026
Replace fixed chunked-prefill settings in an LLM serving engine with a feedback controller that decreases the number of prompt tokens processed per scheduling quantum as GPU saturation or long-context load increases. The controller targets a high-quantile bound on the absolute GPU-power ramp while preserving the existing peak-power ceiling and measuring the resulting latency-throughput tradeoff.
Useful7/10
Difficulty5/10
Novelty6/10
△ Mechanism confirmed, baseline not beaten
2026
Partition a neural network into N interacting modules and constrain the Jacobian of its implicit residual map to be block diagonally dominant. Each module can compute its update locally while cross-module coupling is monitored through a normalized block-row margin. The certificate guarantees local nonsingularity of the equilibrium equations and predicts a sharp loss of robustness when the largest BDD ratio approaches one.
Useful7/10
Difficulty6/10
Novelty7/10
✗ Failed on benchmark
2026
Replace a static batch size with a schedule optimized for a prescribed learning-rate schedule and a fixed total number of processed examples. Steps whose stochastic-gradient noise has a large effect on the paper's loss bound receive larger batches, with the weighting determined by the future learning-rate tail rather than by a hand-designed warmup or cooldown rule.
Useful7/10
Difficulty5/10
Novelty5/10
△ Mechanism confirmed, baseline not beaten
2026
Treat token-to-expert assignments as a bipartite routing graph and penalize patterns that leave token groups sharing too few experts. Enforcing sublinear expansion makes routing diversity explicit: even a moderately sized group of tokens must reach many distinct experts, reducing expert collapse and improving load balance without requiring every token to use every expert.
Useful7/10
Difficulty4/10
Novelty6/10
△ Mechanism confirmed, baseline not beaten
2026
Parameterize a trainable weight update as \(\Delta W=UV^{\top}\) with an excessive initial rank \(r\), and penalize active columns using an exact column \(\ell_{2,0}\) penalty. Increase \(\lambda\) along a warm-started path and hard-delete redundant paired columns, producing an automatically selected rank without training a separate model for every candidate rank. Apply scale balancing after each update so pruning decisions are invariant to reciprocal rescaling of factor pairs.
Useful7/10
Difficulty5/10
Novelty6/10
✗ Failed on benchmark
2026
Train a cheap shared multi-task probe briefly, extract one semantic embedding per task, and use density-based clustering to determine which tasks should share a neural trunk. After clustering, replace the globally shared trunk by one trunk per discovered cluster, with task heads remaining separate; this preserves cooperation among related tasks while isolating destructive task interactions.
Useful7/10
Difficulty5/10
Novelty5/10
△ Mechanism confirmed, baseline not beaten
2026
Replace a portion of quadratic key-value attention or an external episodic table with a per-sample matrix fast memory updated by rank-one delta corrections. The memory directly learns a linear key-to-value map and can be carried across sequence segments, providing cheap online adaptation with constant state size per head.
Useful7/10
Difficulty5/10
Novelty5/10
✗ Failed on benchmark
2026
Replace deterministic LoRA importance scores with one-sided tests of whether each rank-one update has population contribution at least a user-selected threshold. Maintain empirical contribution samples during fine-tuning, estimate their uncertainty, and prune the components with the weakest statistical evidence while respecting the target rank budget. The method should avoid deleting components merely because their latest minibatch gradient was small.
Useful7/10
Difficulty5/10
Novelty7/10
✗ Failed on benchmark
2026
Replace a neural-network optimizer's globally fixed learning-rate geometry with an adaptive quadratic trust region. At every update, construct a local curvature model, accept or reject the step using the ratio between realized and predicted loss decrease, and expand or contract the radius accordingly; the same controller should automatically become conservative in nonconvex regions and Newton-like near a well-conditioned minimum.
Useful7/10
Difficulty6/10
Novelty6/10
△ Mechanism confirmed, baseline not beaten
2026
Penalize short positive feedback cycles in an iterative neural module by suppressing products of absolute Jacobian blocks around the cycle. This targets the mechanism responsible for exponential temperature sensitivity rather than merely penalizing the total Jacobian norm, allowing strong feed-forward paths while controlling recurrent amplification.
Useful7/10
Difficulty7/10
Novelty7/10
△ Mechanism confirmed, baseline not beaten
2026
Use small-gain diagnostics to jointly learn module normalization and a communication partition rather than imposing a fixed global spectral constraint. Clusters should be formed around high-gain feedback loops, because grouping weakly related modules cannot improve the certificate and only adds bookkeeping.
Useful7/10
Difficulty7/10
Novelty7/10
✗ Failed on benchmark
2026
Replace a capacity-penalty-only MoE router with a nonnegative shadow price for each expert, capacity bucket, or hardware resource. Route each token using predicted utility minus the relevant price, while computing a decomposed optimistic objective that certifies how much utility remains above the feasible routed value.
Useful7/10
Difficulty4/10
Novelty5/10
△ Mechanism confirmed, baseline not beaten
2026
Grow a neural network by appending a trainable block together with an analytically initialized inverse block, so the newly added depth is exactly the identity at insertion time. After insertion, untie and optimize the two blocks independently; this preserves the current function while providing additional trainable degrees of freedom. For architectures with one expensive mixing operation followed by cheap channelwise blocks, the same construction can increase depth without repeatedly paying for…
Useful7/10
Difficulty5/10
Novelty5/10
✗ Mechanism failed
2026
Replace a globally chosen constant learning rate with a blockwise rate calibrated to the local flatness exponent of the objective. If the local Hessian decays like \(\|x-x_\star\|^{m-2}\), choose the rate so that the predicted stationary parameter radius \(\alpha^{1/m}\) matches a prescribed exploration or optimization radius, rather than incorrectly using the quadratic rule \(\sqrt{\alpha}\).
Useful7/10
Difficulty5/10
Novelty7/10
△ Mechanism confirmed, baseline not beaten
2026
Train a generative watermark so that its information about the payload is deliberately distributed across positions or overlapping windows instead of being concentrated in a few easily cropped tokens. The objective uses the paper's conditional information profile and the footprint-resolution lower bound to select the smallest carrier support compatible with a target crop size, while preserving generation quality outside that support.
Useful7/10
Difficulty5/10
Novelty7/10
△ Mechanism confirmed, baseline not beaten
2026
Replace dense query-key attention with an adaptive cross approximation constructed from selected query and key pivot tokens. At each rank, choose the pivot pair that removes large estimated residual energy, update the residual by a rank-1 cross correction, and stop when the residual estimate reaches a target tolerance. The resulting factorization computes approximate attention using a small number of landmark interactions while adapting to the actual token distribution.
Useful7/10
Difficulty6/10
Novelty5/10
△ Mechanism confirmed, baseline not beaten
2026
Use the extreme-eigenvector marginal test to decide whether a Kronecker preconditioner is condition-optimal, rather than blindly running expensive factor refinement. If the certificate fails, construct a low-cost factor correction from the mismatch between tensor marginals of the worst-conditioned spectral states and accept it only with a condition-number line search.
Useful7/10
Difficulty7/10
Novelty8/10
✓✓ Beats tuned baseline
2026
Route tokens to experts using Laguerre cells defined by the minimum control energy needed to move a token embedding to each expert prototype, rather than by Euclidean distance or an unconstrained learned router logit. Per-expert dual weights deform the cells so that minibatch routing follows prescribed expert capacities, giving a geometrically interpretable alternative to auxiliary load-balancing losses.
Useful7/10
Difficulty6/10
Novelty6/10
△ Mechanism confirmed, baseline not beaten
2026
Estimate an expensive fine-model trace or quadratic-form quantity using a telescoping sum over cheap-to-expensive neural approximations. Allocate many probes to cheap levels and only a few probes to the expensive level, exploiting strong correlation between adjacent levels to reduce variance at fixed compute. Candidate levels include truncated Transformer depth, reduced width, low-rank curvature, coarser graph resolution, or progressively tighter implicit-solver tolerances.
Useful7/10
Difficulty6/10
Novelty6/10
✗ Mechanism failed
2026
Use the activation singular-value spectrum to allocate a fixed zeroth-order parameter budget across layers instead of assigning the same rank everywhere. Layers with a large discarded singular-value tail receive more coefficient directions, while spectrally compressible layers use smaller adapters, preserving the gradient-relevant subspace under a global memory and query budget.
Useful7/10
Difficulty4/10
Novelty6/10
Audited (legacy)
2026
Replace uniform embedding dimensions with a globally budgeted allocation based on the estimated spectral complexity of each categorical feature. Tables whose category representations have large leading singular-value energy receive more dimensions, while high-cardinality tables are penalized because each extra dimension consumes more parameters.
Useful7/10
Difficulty4/10
Novelty6/10
Unverified
2026
Replace dense token attention with K asynchronous local nonlinear consensus updates on a sparse token graph. Each selected token is moved to the unique incident p-mean of its neighbors, producing a tunable message-passing operator that is averaging for p=2 and nonlinear for other p.
Useful6/10
Difficulty5/10
Novelty6/10
Unverified
2026
Propagate uncertain inputs or parameter perturbations through a neural network with zonotopes, and choose the reduction rule after each layer using short lookahead that predicts downstream certified-bound error. Candidate reducers can include largest-generator retention, box conversion, and norm-based merging; unlike a fixed policy, the chosen rule depends on the current generator geometry and remaining network depth. The goal is tighter robustness bounds at the same generator budget, or the…
Useful6/10
Difficulty6/10
Novelty7/10