Inference

Artificial Intelligence / 13 Sep 2026 / 8 min read

Balance Sparse Mixture-of-Experts Routing Under Capacity Limits

A sparse mixture-of-experts layer does not send every token through every parameter block. A router scores the available experts, selects a small subset for each token, and dispatches token representations only to those selected experts. That conditional computation is the main attraction of sparse MoE designs, but it also creates a resource-allocation problem inside the model. The router can prefer the same experts for many tokens. Hardware, meanwhile, has finite buffers and communication capacity. A routing policy that looks reasonable from token scores alone can therefore create overloaded experts, idle experts, uneven communication, or discarded assignments.

Artificial Intelligence Transformers

Artificial Intelligence / 12 Sep 2026 / 11 min read

Understand Sparse Mixture-of-Experts Routing

Understand Sparse Mixture-of-Experts Routing A larger neural network can represent more functions, but using every parameter for every input makes each forward pass expensive. Sparse mixture-of-experts (MoE) models take a different approach: keep many parameter groups available, then activate only a small subset for each token. That sounds like a simple efficiency trick, but routing changes much more than arithmetic cost. It affects training stability, accelerator communication, memory requirements, batching, and the meaning of a model’s total parameter count.

Artificial Intelligence Mixture of Experts

Artificial Intelligence / 12 Sep 2026 / 9 min read

Stream Long LLM Sessions with Attention Sinks

Stream Long LLM Sessions with Attention Sinks Long-running LLM sessions create a simple resource problem: every generated token can add keys and values to the attention cache. Keep the entire history and memory use keeps growing. Keep only the newest tokens and some transformer models degrade sharply once older cache entries disappear. Attention sinks provide a useful middle ground for compatible models. Instead of retaining the full KV cache, keep a small group of initial tokens plus a moving window of recent tokens. The cache stays bounded, yet the model can remain much more stable than with a recent-token window alone.

Artificial Intelligence LLM

Artificial Intelligence / 12 Sep 2026 / 9 min read

Stabilize Image Model Predictions with Test-Time Augmentation

Stabilize Image Model Predictions with Test-Time Augmentation An image classifier can give slightly different answers when the same subject is cropped, mirrored, or resized in a way that preserves its meaning. If those transformations are valid for the task, relying on one view leaves useful evidence unused. Test-time augmentation (TTA) runs inference on several valid views of one input and combines their predictions into a final result. TTA is simple to describe, but safe use depends on details that are easy to miss. A transformation must preserve the target, structured outputs may need to be mapped back before aggregation, probability averaging can affect calibration, and every extra view consumes inference capacity.

Artificial Intelligence Computer Vision

Artificial Intelligence / 12 Sep 2026 / 7 min read

Speculative Decoding Depends on Draft Acceptance

Autoregressive generation normally commits one token after each model pass, creating a serial dependency across the output sequence. Speculative decoding changes that execution pattern. A cheaper draft process proposes several future tokens, then the target model evaluates those proposals together and determines which tokens can be committed. The attraction is fewer serial target-model iterations per generated token. That does not make speculative decoding an automatic latency reduction. Its useful operating point depends on how cheaply candidates are produced, how many survive verification, and how much extra work the target model performs while checking them.

Artificial Intelligence LLM

Artificial Intelligence / 12 Sep 2026 / 8 min read

Reduce Neural Network Inference Cost with Early Exits

Reduce Neural Network Inference Cost with Early Exits A deep neural network normally applies every block to every input, even when an intermediate representation already supports a confident prediction. Early-exit inference changes that fixed-depth path. It attaches prediction heads at intermediate points and lets selected inputs stop before the final block. The appeal is conditional computation: easy cases can consume less compute while ambiguous cases retain access to the full network. The difficult part is deciding when an intermediate prediction is reliable enough to return. A poor exit policy can save computation by silently moving errors toward the shallow heads.

Artificial Intelligence Neural Networks

Artificial Intelligence / 12 Sep 2026 / 10 min read

Improve LLM Generation with Contrastive Decoding

Improve LLM Generation with Contrastive Decoding A language model can assign high probability to text that is fluent but bland, repetitive, or overly driven by common patterns. Sampling adds variety, but increasing randomness can also admit weak continuations. Contrastive decoding takes a different route: compare a stronger model with a weaker reference model at each generation step, then favor tokens that the stronger model supports more distinctly. The method changes decoding rather than model parameters. It can therefore be useful when you control inference for compatible models and want to experiment with generation quality without another training run. The extra model pass is not free, and the method needs a plausibility guard to avoid promoting strange tokens.

Artificial Intelligence LLM

Artificial Intelligence / 12 Sep 2026 / 6 min read

Exit Transformer Classifiers Early with Entropy Thresholds

A transformer classifier normally sends every input through every layer, even when an intermediate representation already supports a concentrated class prediction. Entropy-based early exit changes that fixed-depth behavior. Prediction heads attached to intermediate layers estimate class distributions, and inference can stop once a distribution passes a configured entropy threshold. The mechanism makes model depth input-dependent. Some inputs may leave after relatively few layers, while uncertain inputs continue through more of the network. That flexibility also introduces a new source of error: an intermediate head can be confident and still be wrong.

Artificial Intelligence Transformers

Artificial Intelligence / 12 Sep 2026 / 6 min read

Control Diffusion Conditioning with Classifier-Free Guidance

A conditional diffusion model can follow its conditioning signal more strongly at sampling time without a separate classifier. Classifier-free guidance does this by evaluating a model in conditional and unconditional modes, then amplifying the difference between those predictions. That difference is the central mechanism. The guidance scale does not simply make a prompt louder in an abstract sense. It changes the denoising prediction along a direction defined by what the conditioning input contributes relative to an unconditional prediction at the same noisy state.

Artificial Intelligence Diffusion Models

Artificial Intelligence / 12 Sep 2026 / 6 min read

Contrast Language Model Logits with Expert-Amateur Decoding

A language model can assign high probability to tokens that are fluent but generic. Contrastive decoding changes token selection by comparing a stronger expert model with a weaker amateur model at the same generation position. A token becomes attractive when the expert favors it more strongly than the amateur does. The comparison is not an unrestricted subtraction across the vocabulary. The original method also keeps candidate tokens inside a plausibility set defined by the expert. That constraint matters because a large expert-amateur score gap can otherwise promote a token that both models consider implausible.

Artificial Intelligence Large Language Models

Artificial Intelligence / 12 Sep 2026 / 7 min read

Bound KV Cache Growth in Streaming LLM Inference

Autoregressive transformer inference normally keeps key and value states from earlier tokens so each new token can attend to prior context without recomputing those states. The cache grows with sequence length. For a long-running stream, that growth eventually becomes a memory constraint even when generation itself continues one token at a time. KV cache eviction puts a bound on that state by discarding selected cached positions. The memory effect is straightforward: fewer retained key-value pairs occupy less cache space. The model effect is more subtle. Once a position is removed, later attention layers cannot use its cached key and value in the ordinary attention calculation. An eviction policy therefore changes both resource use and the effective attention history.

Artificial Intelligence LLM

Artificial Intelligence / 11 Sep 2026 / 10 min read

Cut Neural Network Inference Cost with Early Exits

Cut Neural Network Inference Cost with Early Exits A neural network usually spends the same depth of computation on every input. That is convenient, but not every input needs the same effort. A clear image of a stop sign may be classified correctly after relatively shallow processing, while an occluded sign may need the full network. Early-exit inference adds intermediate prediction points to a model and lets sufficiently confident inputs stop before the final layer. The aim is not to make every request cheaper. It is to spend less computation on easier cases while preserving a deeper path for harder ones.

Artificial Intelligence Neural Networks

Artificial Intelligence / 10 Sep 2026 / 10 min read

Temperature in LLM Sampling

A language model can produce very different continuations from the same prompt even when its weights and context haven’t changed. One of the controls behind that variation is temperature. Temperature is often described as a creativity knob. That description is convenient but incomplete. Temperature doesn’t add ideas to a model, improve its knowledge, or directly control factual accuracy. It changes the probability distribution used to choose the next token. The practical effect depends on what the model already considers plausible at that step.

Artificial Intelligence LLM

Artificial Intelligence / 09 Sep 2026 / 9 min read

LLM Sampling: Temperature, Top-K, and Top-P

A language model does not normally produce a single inevitable next token. Given a prefix, it assigns scores to many possible tokens. A decoding algorithm then decides how to turn those scores into the next output. That last step matters. If you sample too freely, a model can drift into unlikely continuations. If you restrict sampling too aggressively, outputs can become repetitive or lose useful variation. Parameters such as temperature, top-k, and top-p control different parts of this trade-off, so treating them as interchangeable “creativity settings” leads to confusing results.

Artificial Intelligence LLM

Artificial Intelligence / 09 Sep 2026 / 11 min read

Control Beam Search Length Bias with Length Normalization

Beam search is a common way to decode sequence models when choosing the most likely token at every step is too shortsighted. It keeps several partial candidates alive, expands them, and repeatedly retains the strongest alternatives. There is a subtle problem: the score used for a sequence usually accumulates one log-probability per generated token. Because token probabilities are at most 1, their log-probabilities are normally non-positive. Extending a sequence therefore tends to make its raw cumulative score smaller. When finished candidates of different lengths compete directly, this can create a preference for outputs that end too early.

Artificial Intelligence Machine Learning

Artificial Intelligence / 09 Sep 2026 / 10 min read

Choose Consensus Outputs with Minimum Bayes Risk Decoding

A generative model can assign high probability to an output that is not the most useful answer for your application. This is especially visible when several different outputs are plausible: a translation can have multiple valid phrasings, a summary can emphasize different details, and a structured generator can produce several semantically similar candidates. Greedy decoding chooses locally likely tokens. Beam search searches for a high-probability sequence. Sampling gives you diverse candidates. None of those methods, by itself, asks a different question that is often closer to the application goal: which candidate agrees best with the distribution of plausible outputs?

Artificial Intelligence LLM

Artificial Intelligence / 08 Sep 2026 / 9 min read

Use Test-Time Augmentation Without Hiding Model Errors

A model usually makes one prediction from one representation of an input. That is convenient, but the representation may contain accidental details that should not determine the answer. A product photo can be shifted a few pixels. A scanned digit can be slightly rotated. A crop can place the object closer to one edge than another. Test-time augmentation (TTA) asks the trained model to predict several valid transformations of the same input and then combines those predictions. The technique can make inference less dependent on one particular view, but it also increases compute and can make predictions worse when the transformations change information that matters to the label.

Artificial Intelligence Machine Learning

Artificial Intelligence / 08 Sep 2026 / 10 min read

Avoid Tokenization Boundary Failures in LLM Generation

A language model application usually treats a prompt as text: provide a prefix, then ask the model to continue it. The model sees something more specific. Its tokenizer first converts that text into tokens, and the end of the prompt forces the last token to end at exactly that position. That detail can matter when the prompt ends at a character position that would normally fall inside a larger token if the prompt and its continuation were tokenized together. The resulting tokenization boundary problem, also called the partial token problem, can make an otherwise natural continuation unexpectedly unlikely.

Artificial Intelligence LLM

Artificial Intelligence / 07 Sep 2026 / 10 min read

Repetition Controls for Language Model Generation

A language model can produce fluent text and still get stuck repeating a phrase, sentence pattern, or idea. A support reply may restate the same apology three times. A summarizer may loop over one point. A long generation can begin copying a short phrase again and again. The tempting fix is to turn up a “repetition” setting until the duplicate text disappears. That can solve one symptom while creating another: names become awkward, code becomes invalid, required terms disappear, or a model avoids repeating words that the task genuinely needs.

Artificial Intelligence LLM

Artificial Intelligence / 07 Sep 2026 / 10 min read

Neural Network Pruning: Sparsity, Structure, and Speed

A neural network can contain parameters that contribute little to its useful predictions. Removing some of them can reduce storage or computation, but there is an important trap: a model with fewer nonzero weights is not automatically a model that runs faster. That distinction matters when developers use pruning to compress a trained network. The pruning rule determines what disappears, the hardware and runtime determine whether the resulting structure can be exploited, and the evaluation procedure determines whether the saved resources are worth any quality loss.

Artificial Intelligence Neural Networks

Artificial Intelligence / 07 Sep 2026 / 9 min read

Improve Reasoning Reliability with Self-Consistency

A language model can reach different answers to the same reasoning problem depending on how generation unfolds. One sampled path may make an arithmetic mistake, another may misread a condition, and a third may reach the correct result. If an application trusts only one path, its answer depends heavily on that single generation. Self-consistency uses this variability instead of trying to eliminate it. It samples several reasoning paths for the same problem, extracts their final answers, and chooses the answer supported by the largest share of the samples. The technique is an inference-time strategy: it does not require changing model weights.

Artificial Intelligence LLM

Artificial Intelligence / 06 Sep 2026 / 10 min read

Use Best-of-N Sampling to Spend More Compute at Inference

A language model can produce several plausible answers to the same prompt. That variability is often treated as noise, but it can also be used deliberately: generate multiple candidates, evaluate them, and return the strongest one. This pattern is called best-of-N sampling. Instead of trusting one generation, the system samples N responses and uses a scoring rule to select one. The extra samples spend more compute at inference time in exchange for more opportunities to find a good response.

Artificial Intelligence LLM

Artificial Intelligence / 06 Sep 2026 / 9 min read

Token and Sequence Biases for LLM Decoding

Sometimes an LLM produces generally good text but makes one narrow decoding choice too often. Perhaps a domain-specific abbreviation should be preferred, a deprecated product name should be discouraged, or a particular token must not appear in generated text. Changing temperature is a poor fit for this problem because temperature affects the whole next-token distribution. Retraining a model is usually excessive when the desired change is local. Token and sequence biases provide a narrower tool: modify selected prediction scores during decoding while leaving the model parameters unchanged.

Artificial Intelligence LLM

Artificial Intelligence / 06 Sep 2026 / 10 min read

Steer Language Models by Editing Hidden Activations

Prompting changes what a language model reads. Fine-tuning changes its parameters. There is another, more experimental way to influence generation: change the model’s internal activations while it runs. This technique is commonly called activation steering or activation engineering. A simple version measures how hidden representations differ between examples that express opposite properties, turns that difference into a steering vector, and adds a scaled version of the vector during inference. The model weights stay unchanged.

Artificial Intelligence Large Language Models